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@ -19,12 +19,12 @@ Credit: Danica Willbanks created and carried out the survey, with the help of se
- Bonus: [A practical exercise in p-hacking (or not)](https://nunosempere.github.io/rat/eamentalhealth/analysis/p-hacking.html)
- 1.4. Bottom line.
2. Does EA have a mental health problem?
3. Is more involvement with EA correlated with mental ilness?
3. Is more involvement with EA correlated with mental illness?
4. Is being mentally ill predictive of answering yes to: "Do you think you could personally benefit from EA community mental health resources?"
5. Is having a mental disorder, or thinking you could potentially benefit from mental health ressources correlated with thinking that providing them is likely to be one of the most effective interventions available?
5. Is having a mental disorder, or thinking you could potentially benefit from mental health resources correlated with thinking that providing them is likely to be one of the most effective interventions available?
6. When is an effective use of the EA Community's resources != the most effective intervention?
7. How does further involment in EA correlate with opinions on the value of mental health resources?
8. How does productivity lost because of mental health problems correlate with opinions on the value of mental health resources (and viceversa)?
7. How does further involvement in EA correlate with opinions on the value of mental health resources?
8. How does productivity lost because of mental health problems correlate with opinions on the value of mental health resources (and vice versa)?
9. How does mental health affect productivity, and how is this mediated by access to healthcare?
- 9.1. How does mental health affect productivity?
- 9.2. How is lost productivity mediated by treatment and access to treatment?
@ -32,7 +32,7 @@ Credit: Danica Willbanks created and carried out the survey, with the help of se
- 9.4. Correlation of the Work Productivity and Impairment Scale with mental health, mediated by an index of access.
- 9.5 The impact of just providing information.
- 9.6 For completeness's sake: All the possible correlations impact ~ productivity
10. How does access to mental health ressources vary with a variety of factors?
10. How does access to mental health resources vary with a variety of factors?
- 10.1. By countries or continent.
- 10.2. By race/ethnicity.
- 10.3. By mental health
@ -42,14 +42,14 @@ Credit: Danica Willbanks created and carried out the survey, with the help of se
#### D. Implications for mental health in EA overall, guided by some insightful comments made by the respondents.
- 1.1. Selection effects in EA.
- 1.2. Do mental health problems stem from EA-specific beliefs?
- 1.3. EA may not have a comparative advantage in providing mental health ressources.
- 1.3. EA may not have a comparative advantage in providing mental health resources.
- 1.4. EA France has something going on
- 1.5. Visceral comparison with global poverty
- 1.6. Moral hazard.
- 1.7. Layers of indirectness and pathways to impact.
- 1.8. A support group for EAs with ADHD
- 1.9. Cheap ressources.
- 1.10. Providing mental health ressources is creepy
- 1.9. Cheap resources.
- 1.10. Providing mental health resources is creepy
#### E. Summary.
@ -135,7 +135,7 @@ EA Survey 2018:
However, in this survey, 54.46% of respondents reported being male, 33.33% of respondents reported being female, and 12.21% described themselves as other (7.59%) or declined to self-identify (4.62%).
#### 1.4. Bottom line:
Editorial bottom line: With respect to age, country, and gender, there seem to be significant but not overwhelming differences. The differences in gender identification might be indicative of selection effects affecting this survey. This is because perhaps people in this third category have higher rates of mental ilness. I have explored this hypothesis at length here: [A practical exercise in p-hacking (or not)](https://nunosempere.github.io/rat/eamentalhealth/analysis/p-hacking.html).
Editorial bottom line: With respect to age, country, and gender, there seem to be significant but not overwhelming differences. The differences in gender identification might be indicative of selection effects affecting this survey. This is because perhaps people in this third category have higher rates of mental illness. I have explored this hypothesis at length here: [A practical exercise in p-hacking (or not)](https://nunosempere.github.io/rat/eamentalhealth/analysis/p-hacking.html).
### 2. Does EA have a mental health problem?
Initially, I was intending to find out the different mental disorder rates in the different countries, and combine that with the distribution in the data. The webpage [Our World in Data](https://ourworldindata.org/mental-health) provides the necessary data:
@ -145,12 +145,12 @@ Initially, I was intending to find out the different mental disorder rates in th
We see that the distribution is broadly similar across the countries among which EA has a presence. Most importantly, it doesn't surpass ~25% in any country, whereas among survey respondents:
- 45% have been diagnosed with one or more mental disorders (from our list).
- 71% either have been diagnosed with one or more mental disorders, or intuit they have one.
- The average number of mental ilnesses respondents have been diagnosed with is 0.82
- The average number of mental ilnesses respondents have either been diagnosed with or intuit they have is 1.68. This number is higher than one because some (many respondents) have more than one disorder. This is not particularly surprising: see [Understanding Comorbid Depression and Anxiety](https://www.psychiatrictimes.com/articles/understanding-comorbid-depression-and-anxiety).
- The average number of mental illnesses respondents have been diagnosed with is 0.82
- The average number of mental illnesses respondents have either been diagnosed with or intuit they have is 1.68. This number is higher than one because some (many respondents) have more than one disorder. This is not particularly surprising: see [Understanding Comorbid Depression and Anxiety](https://www.psychiatrictimes.com/articles/understanding-comorbid-depression-and-anxiety).
Thus, we can conclude with certainty that there are selection effects going on. Whether this is at the level of the EA community or at the survey level is not deducible from the data. That is, it could either be that EA attracts people with mental disorders, or that the survey attracts respondents with mental disorders. Thus, we suggest adding a mental health section to the yearly EA Survey by Rethink Charity.
### 3. Is more involvement with EA correlated with mental ilness?
### 3. Is more involvement with EA correlated with mental illness?
The first four questions in our survey relate to involvement with EA:
- How involved are you in the Effective Altruism Community?
@ -158,17 +158,17 @@ The first four questions in our survey relate to involvement with EA:
- How much impact do EA ideas have on your life?
- Do you donate part of your income to GiveWell recommended charities?
And two measures of mental ilness:
- A. A binary variable indicating whether a person was diagnosed with any mental ilness (from our list) or not.
- B. A binary variable indicating whether a person was diagnosed with any mental ilness / think they have a mental ilness, or not
- C. An integer variable with the number of mental ilnesses a person has been diagnosed with.
- D. An integer variable with the number of mental ilnesses a person has been diagnosed with, or thinks they have.
And two measures of mental illness:
- A. A binary variable indicating whether a person was diagnosed with any mental illness (from our list) or not.
- B. A binary variable indicating whether a person was diagnosed with any mental illness / think they have a mental illness, or not
- C. An integer variable with the number of mental illnesses a person has been diagnosed with.
- D. An integer variable with the number of mental illnesses a person has been diagnosed with, or thinks they have.
We have run 20 linear models, regressing each of our measures of mental ilness with the answers to each of the four questions, and their sum (where verbal scales are converted to numerical ones when required. For example, a {"No", "Yes"} is converted to {0,1}. As a technical note, whether it's converted to {0,1} or to {1,2} doesn't affect the regression coefficient, just the intercept)
We have run 20 linear models, regressing each of our measures of mental illness with the answers to each of the four questions, and their sum (where verbal scales are converted to numerical ones when required. For example, a {"No", "Yes"} is converted to {0,1}. As a technical note, whether it's converted to {0,1} or to {1,2} doesn't affect the regression coefficient, just the intercept)
In 17/20 cases, we get a positive but insignificant effect. One case is ambiguous and instill insignificant, and the remaining 2/20 cases are negative but still insignificant. That is, further involvement with EA across all but one model makes one less likely to have mental ilnesses, and to have fewer mental ilnesses, but at no point does this reach significance thresholds.
In 17/20 cases, we get a positive but insignificant effect. One case is ambiguous and instill insignificant, and the remaining 2/20 cases are negative but still insignificant. That is, further involvement with EA across all but one model makes one less likely to have mental illnesses, and to have fewer mental illnesses, but at no point does this reach significance thresholds.
For example, consider whether attending EA meeting has a positive effect on the binary variable: has been diagnosed with a mental health ilness.
For example, consider whether attending EA meeting has a positive effect on the binary variable: has been diagnosed with a mental health illness.
```
> summary(lm(A$m_ill_or_not ~ A$Do.you.attend.EA.meetings.))
@ -182,7 +182,7 @@ A$Do.you.attend.EA.meetings.Yes, occasionally
A$Do.you.attend.EA.meetings.Yes, often 0.1042 0.2926 0.356 0.722
```
The key column is "Estimate". Smaller numbers are better, and we see that the more often one goes, the less likely is one to have been diagnosed with a mental ilness. No > No, but I regularly participate in an EA online group > Yes, occasionally ~ Yes often.
The key column is "Estimate". Smaller numbers are better, and we see that the more often one goes, the less likely is one to have been diagnosed with a mental illness. No > No, but I regularly participate in an EA online group > Yes, occasionally ~ Yes often.
In the interest of total disclosure, [here](https://nunosempere.github.io/rat/eamentalhealth/analysis/regressions_EA_mental_health.html) is a link with the 20 regressions carried out, and the code used to generate them.
@ -192,18 +192,18 @@ Curiously, I was kind of expecting the opposite result. It would just have been
#### 4. Is being mentally ill predictive of answering yes to: "Do you think you could personally benefit from EA community mental health resources?"
The two questions which directly ask about mental ilness are:
The two questions which directly ask about mental illness are:
- Which of these conditions have you been formally diagnosed with?
- Which of these conditions do you think you may have, but have never been formally diagnosed with?
And the questions which ask about benefiting from mental health ressources are:
And the questions which ask about benefiting from mental health resources are:
- Do you think you could personally benefit from EA community mental health resources?
- Which if any of the following resources do you think you could potentially benefit from?
I model having a mental ilness as before. Independent on the modelization, the answer our question is yes, people with mental ilnesses are more likely to think they can benefit from mental health ressources, with p-values ranging from 0.0004736 to
I model having a mental illness as before. Independent on the modelization, the answer our question is yes, people with mental illnesses are more likely to think they can benefit from mental health resources, with p-values ranging from 0.0004736 to
7.612e-12. The obvious result is indeed obvious, and it serves as a proof of concept: we have enough power to find out *some* things.
### 5. Is having a mental disorder, or thinking you could potentially benefit from mental health ressources, correlated with thinking that providing them is likely to be one of the most effective interventions available?
### 5. Is having a mental disorder, or thinking you could potentially benefit from mental health resources, correlated with thinking that providing them is likely to be one of the most effective interventions available?
Take a moment to make your predictions before you read ahead.
@ -224,7 +224,7 @@ A[, 26] == "Yes"TRUE 0.29018 0.05558 5.221 3.32e-07 ***
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
```
I had forgotten how deep the waters are. If people think they can benefit from mental health ressources, the probability that they agree or strongly agree with the statement "I believe that offering mental health resources to its members is an effective use of the EA Community's resources" goes up from 42.857% to 29.018% + 42.857% = 71.875%, with a p-value of 3.321e-07!
I had forgotten how deep the waters are. If people think they can benefit from mental health resources, the probability that they agree or strongly agree with the statement "I believe that offering mental health resources to its members is an effective use of the EA Community's resources" goes up from 42.857% to 29.018% + 42.857% = 71.875%, with a p-value of 3.321e-07!
Similarly, the probability that they agree with the statement "I believe that offering mental health resources to effective altruists is NOT likely to be one of the most effective interventions available" goes down from 32.571% to 32.571%-16.165% = 16.406%, with a p-value of 0.001409!
@ -241,16 +241,16 @@ Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’
```
Several interpretations present themselves.
1: This is a bias, a component of self-interest. Perhaps people who have mental ilnesses are in more pain, and people who feel pain are more selfish. If this was an op-ed piece, much could be said about how the above is why we take care of cultivating rationality, and I'd propose that this topic is more conducive to research analysis of the sort in which QALYs are estimated, and that asking the broad public for opinions is not that valuable.
1: This is a bias, a component of self-interest. Perhaps people who have mental illnesses are in more pain, and people who feel pain are more selfish. If this was an op-ed piece, much could be said about how the above is why we take care of cultivating rationality, and I'd propose that this topic is more conducive to research analysis of the sort in which QALYs are estimated, and that asking the broad public for opinions is not that valuable.
2: People who feel mental pain have *grokked* negative utilitarianism, or similarly have different intuitions about the matter.
3: "Those who believe they could personally benefit might assess ressources as more valuable precisely because they benefit themselves" is logically equivalent to "Those who don't believe they could personally benefit might assess resources as less valuable precisely because they wouldn't benefit themselves", but in the second case, the connotation is that the bias is on the part of the mentally healthy people. More neutrally, this is perhaps a case of the typical mind fallacy, in which both mentally healthy and mentally ill people use the heuristic of estimating the typical EA mind as similar to their mind, and thus reach different conclusions.
3: "Those who believe they could personally benefit might assess resources as more valuable precisely because they benefit themselves" is logically equivalent to "Those who don't believe they could personally benefit might assess resources as less valuable precisely because they wouldn't benefit themselves", but in the second case, the connotation is that the bias is on the part of the mentally healthy people. More neutrally, this is perhaps a case of the typical mind fallacy, in which both mentally healthy and mentally ill people use the heuristic of estimating the typical EA mind as similar to their mind, and thus reach different conclusions.
4: Others.
Personal comment: After having considered the above interpretations, I still assign most of the probability mass to interpretation 1: that this mechanism is [due to self-interest, or structurally similar to it](http://elephantinthebrain.com). That seems to me to be the most straightforward and simple hypothesis, whereas I see the other ones as sophisticated ex post facto rationalizations, that is, of being the answers to the following question: now that we know that this happened, what other mechanisms could explain the same phenomenon while not accusing anyone of being influenced by self-interest? (that has been, incidentally, the algorithm used to generate these ideas). To express the above numerically, I think that the intuitive odds which I assign to (Interpretation 1: Interpretation 2: Interpretation 3) would broadly and roughly be more like (80:2:10), and not at all like (33:33:33). This judgement may appear, and in fact be, one-sided.
Readers are welcome to reach their own conclusions.
The effect is very robust to different modelizations: regressing instead on empty answers to question 25: "Which if any of the following resources do you think you could potentially benefit from?", regressing on whether the respondents have a mental disorder instead of whether they say they'd benefit from mental health ressources, including "Not sure" answers in the regression, etc.
The effect is very robust to different modelizations: regressing instead on empty answers to question 25: "Which if any of the following resources do you think you could potentially benefit from?", regressing on whether the respondents have a mental disorder instead of whether they say they'd benefit from mental health resources, including "Not sure" answers in the regression, etc.
Here is the above, presented visually
@ -285,10 +285,10 @@ However, I insisted in adding the second question because the connotations are s
As it turns out, only 30% of respondents gave the same answer to the two questions. This is not correlated with mental disorders, age, sex, gender, impact of EA ideas in one's life or involvement in the EA community. It is, however, weakly correlated with donating to GiveWell recommended charities.
### 7. How does further involment in EA correlate with opinions on the value of mental health resources?
The correlation between involvement in EA and having a positive opinion of providing mental health ressources is positive, but small and not significant at all. I wouldn't read anything into this, but I am reporting this because I ran the regression.
### 7. How does further involvement in EA correlate with opinions on the value of mental health resources?
The correlation between involvement in EA and having a positive opinion of providing mental health resources is positive, but small and not significant at all. I wouldn't read anything into this, but I am reporting this because I ran the regression.
The above is true both if we create a dummy for each possible answer (which are: "Strongly disagree", "Disagree", "", "Neutral/Not sure", "Agree", "Strongly agree") and also if we instead convert the answers into a number ("Strongly disagree" = -2, "Disagree" = -1, ""=0, "Neutral/Not sure"=0, "Agree"=1, "Strongly agree"=2) and run a regression on that. For that matter, the switch function in R proved usatisfactory for working on strings; I wrote one more suited to my purposes and sufficient for n=303.
The above is true both if we create a dummy for each possible answer (which are: "Strongly disagree", "Disagree", "", "Neutral/Not sure", "Agree", "Strongly agree") and also if we instead convert the answers into a number ("Strongly disagree" = -2, "Disagree" = -1, ""=0, "Neutral/Not sure"=0, "Agree"=1, "Strongly agree"=2) and run a regression on that. For that matter, the switch function in R proved unsatisfactory for working on strings; I wrote one more suited to my purposes and sufficient for n=303.
```
switch2 <- function(Elements, Compare_With, Output_if_found, Output_if_Else){
@ -328,11 +328,11 @@ The two questions which ask about productivity lost because of mental health pro
- During the past 14 days, how many hours did you miss from work because of mental health problems? (numerical answer)
- During the past 14 days, how much did mental health problems affect your productivity while you were working? (numerical answer)
And the two questions which ask about effectiveness of providing mental health ressources are:
And the two questions which ask about effectiveness of providing mental health resources are:
- "I believe that offering mental health resources to its members is an effective use of the EA Community's resources."
- "I believe that offering mental health resources to effective altruists is NOT likely to be one of the most effective interventions available."
Under a wide range of modelizations / operationalizations, there is ~0 correlation between the two sets of answers. The one exception is if we consider a dummy variable for "hours lost due to mental ilness > 0". In that case, respondents who did lose >0 hours are somewhat more likely to support offering mental health resources, but this does not reach significant levels.
Under a wide range of modelizations / operationalizations, there is ~0 correlation between the two sets of answers. The one exception is if we consider a dummy variable for "hours lost due to mental illness > 0". In that case, respondents who did lose >0 hours are somewhat more likely to support offering mental health resources, but this does not reach significant levels.
This is surprising, because a positive opinion of offering mental health resources is strongly correlated with thinking you could benefit from them, thinking you could benefit from them is strongly correlated with having a mental condition, and having a mental condition is strongly correlated with losing hours because of mental health.
@ -358,11 +358,11 @@ Of course the relation "(significantly) correlated to" is not transitive in theo
What is the effect of mental health on productivity, and how does access to healthcare mediate it?
#### 9.1. How does mental health affect productivity?
We can correlate our four markers of mental ilness on our two measures of lost productivity lost. However, the correlation runs both ways: from the data we cannot deduce whether people with mental conditions lose productivity, or whether losing productivity (i.e., losing a job) makes people more likely to be mentally ill.
We can correlate our four markers of mental illness on our two measures of lost productivity lost. However, the correlation runs both ways: from the data we cannot deduce whether people with mental conditions lose productivity, or whether losing productivity (i.e., losing a job) makes people more likely to be mentally ill.
If we assume that the effect is purely unidirectional (mental ilness -> lost productivity), three highlights are:
If we assume that the effect is purely unidirectional (mental illness -> lost productivity), three highlights are:
- Being diagnosed with one or more mental conditions would cause a loss of ~9 hours per 2 weeks.
- Each additional diagnosed mental ilness would cause a loss of ~5 hours per 2 weeks.
- Each additional diagnosed mental illness would cause a loss of ~5 hours per 2 weeks.
- On a subjective 10 point scale, answers to the question ("During the past 14 days, how much did mental health problems affect your productivity while you were working?") increase by ~2 points if the respondent perceives they have at least 1 mental condition.
In total, we run 12 regressions, which are fully documented below. The casual reader is welcome to skip them, and continue to the next section.
@ -408,31 +408,31 @@ A$m_ill_or_not2 & !A$m_ill_or_notTRUE 3.4571 2.3352 1.480 0.140
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# Call: lm(formula = hours_lost ~ A$num_mental_ilnesses)
# Call: lm(formula = hours_lost ~ A$num_mental_illnesses)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.174 1.051 2.069 0.0395 *
A$num_mental_ilnesses 4.856 0.767 6.331 9.58e-10 ***
A$num_mental_illnesses 4.856 0.767 6.331 9.58e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# Call: lm(formula = hours_lost ~ A$num_mental_ilnesses2)
# Call: lm(formula = hours_lost ~ A$num_mental_illnesses2)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.6053 1.2060 -0.502 0.616
A$num_mental_ilnesses2 4.0640 0.5317 7.643 3.33e-13 ***
A$num_mental_illnesses2 4.0640 0.5317 7.643 3.33e-13 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# Call: lm(formula = hours_lost ~ A$num_mental_ilnesses + A$num_mental_ilnesses2)
# Call: lm(formula = hours_lost ~ A$num_mental_illnesses + A$num_mental_illnesses2)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.5355 1.2055 -0.444 0.657
A$num_mental_ilnesses 1.4484 1.0981 1.319 0.188
A$num_mental_ilnesses2 3.3057 0.7826 4.224 3.25e-05 ***
A$num_mental_illnesses 1.4484 1.0981 1.319 0.188
A$num_mental_illnesses2 3.3057 0.7826 4.224 3.25e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
@ -463,27 +463,27 @@ A$m_ill_or_not2 2.9226 0.2828 10.335 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# Call: lm(formula = effect_while_working ~ A$num_mental_ilnesses)
# Call: lm(formula = effect_while_working ~ A$num_mental_illnesses)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.1341 0.1630 19.228 <2e-16 ***
A$num_mental_ilnesses 1.1130 0.1185 9.394 <2e-16 ***
A$num_mental_illnesses 1.1130 0.1185 9.394 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# Call: lm(formula = effect_while_working ~ A$num_mental_ilnesses2)
# Call: lm(formula = effect_while_working ~ A$num_mental_illnesses2)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.43830 0.18171 13.42 <2e-16 ***
A$num_mental_ilnesses2 0.95910 0.07998 11.99 <2e-16 ***
A$num_mental_illnesses2 0.95910 0.07998 11.99 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
```
#### 9.2. How is this mediated by treatment and access to treatment?
So unsurprisingly, having a mental ilness decreases both work hours, and self-reported productivity when working. Thus, a pressing question is whether therapy has an effect on that.
So unsurprisingly, having a mental illness decreases both work hours, and self-reported productivity when working. Thus, a pressing question is whether therapy has an effect on that.
Two questions ask about access to treatment:
- "I am currently receiving the mental healthcare I need."
@ -567,7 +567,7 @@ Receiving_positive[m_ill3]TRUE -4.428 4.018 -1.102 0.273
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
```
Note that, in none of the above cases we've gotten a significant effect. This is because outliers, for example, respondents who lost a job because of mental ilness, and thus ~40 hours per week, make the error bars huge. In this case, excluding outliers doesn't make sense at all; instead, knowing where the error comes from, I think it makes sense to not get hung up on p-values. As a proof of concept, here is the same regression as above, but considering the logarithm of hours lost (which gives less weigh to outliers); we "suddenly" reach significance (p-value of 0.00698). On the flipside, the logarithm of hours lost is not an intuitive unit in which to report results.
Note that, in none of the above cases we've gotten a significant effect. This is because outliers, for example, respondents who lost a job because of mental illness, and thus ~40 hours per week, make the error bars huge. In this case, excluding outliers doesn't make sense at all; instead, knowing where the error comes from, I think it makes sense to not get hung up on p-values. As a proof of concept, here is the same regression as above, but considering the logarithm of hours lost (which gives less weigh to outliers); we "suddenly" reach significance (p-value of 0.00698). On the flip-side, the logarithm of hours lost is not an intuitive unit in which to report results.
```
> summary(lm(log(hours_lost[m_ill3]+1) ~ Receiving_positive[m_ill3]))
@ -579,23 +579,23 @@ Receiving_positive[m_ill3]TRUE -0.7131 0.2596 -2.747 0.00698 **
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
```
Alas, we cannot take the above crosssections at face value. Imagine, for example, that people with lighter mental conditions find it easier to deal with the hassle of finding good treatment, which seems plausible. In that case, the causal arrow would go both ways: (getting good mental healthcare -> healing occurs -> people lose less hours each week), but also (having a lighter mental ilness -> easier to find good mental healthcare) + (having a lighter mental ilness -> loose less work hours). It could also be that, as people regress to the mean with respect to their mental ilness, and become happier, they attribute their improvement to whatever therapy they were receiving, and become more satisfied with it. If we assume that the bias goes in the direction of amplifying the effect of good mental healthcare, the above figures become an upper estimate of its effect.
Alas, we cannot take the above crosssections at face value. Imagine, for example, that people with lighter mental conditions find it easier to deal with the hassle of finding good treatment, which seems plausible. In that case, the causal arrow would go both ways: (getting good mental healthcare -> healing occurs -> people lose less hours each week), but also (having a lighter mental illness -> easier to find good mental healthcare) + (having a lighter mental illness -> loose less work hours). It could also be that, as people regress to the mean with respect to their mental illness, and become happier, they attribute their improvement to whatever therapy they were receiving, and become more satisfied with it. If we assume that the bias goes in the direction of amplifying the effect of good mental healthcare, the above figures become an upper estimate of its effect.
In the ideal case, what I'd like to have is an experiment in which:
- Productivity and severity of mental ilness are measured across a population. Or just utilons.
- Productivity and severity of mental illness are measured across a population. Or just utilons.
- I instantiate a second copy of this world.
- In one copy, I offer the treatment. I notice that some people don't take it, even though they could.
- In another copy, I don't. I notice that some people get something similar to the intervention. If I provide information, maybe some people already knew it. If I provide free treatment, maybe some people are not limited by money, and pay for it themselves. When the treatment is not provided, people don't just sit and do nothing, but try their own things.
- I wait for some time.
- I measure productivity and severity of mental ilness again. Or utilons.
- I measure productivity and severity of mental illness again. Or utilons.
- The difference in between what happened in the two worlds is the impact of my intervention, and divided by the cost, the impact per unit of money.
But I can't have that with the current physics, so the most similar possible thing to the above, and what I'd actually like to have, is something like a randomized trial, in which:
- Productivity and severity of mental ilness are measured across a population
- Productivity and severity of mental illness are measured across a population
- I simulate splitting the two worlds by having a treatment and a control group.
- I offer the intervention to the treatment, but not to the control group.
- Some time passes.
- Productivity and severity of mental ilness are measured again, both in the control and treatment group. The difference in outcomes is an estimate of the impact of the intervention.
- Productivity and severity of mental illness are measured again, both in the control and treatment group. The difference in outcomes is an estimate of the impact of the intervention.
The point being that, at each step that our data deviates from this, errors may arise.
@ -643,9 +643,9 @@ Overall_work_impairment = Overall_work_impairment*100 ## To express this is perc
We will use the percent overall work impairment due to health as our productivity measure.
Now, we can correlate productivity lost with having or not having a mental ilness. Because I'm not sure respondents understood that an answer of 5 on a scale of 1-10 would be interpreted as a 50% reduction in effectiveness, I'm hesitant to interpret this as a percentage. If we speak about points in the abstract:
- When regressing lost productivity on mental conditions diagnosed and intuited: Being diagnosed with a mental condition is correlated with 42 points of lost productivity, and Intuiting one has a mental ilness (as opposed to having been diagnosed with one) is correlated with a lost of ~27 points of lost productivity
- Each additional diagnosed mental ilness is correlated with a ~15 point in productivity loss.
Now, we can correlate productivity lost with having or not having a mental illness. Because I'm not sure respondents understood that an answer of 5 on a scale of 1-10 would be interpreted as a 50% reduction in effectiveness, I'm hesitant to interpret this as a percentage. If we speak about points in the abstract:
- When regressing lost productivity on mental conditions diagnosed and intuited: Being diagnosed with a mental condition is correlated with 42 points of lost productivity, and Intuiting one has a mental illness (as opposed to having been diagnosed with one) is correlated with a lost of ~27 points of lost productivity
- Each additional diagnosed mental illness is correlated with a ~15 point in productivity loss.
The regressions carried out are:
@ -715,10 +715,10 @@ Residual standard error: 24.98 on 268 degrees of freedom
Multiple R-squared: 0.3412, Adjusted R-squared: 0.3363
F-statistic: 69.4 on 2 and 268 DF, p-value: < 2.2e-16
> summary(lm(Overall_work_impairment~A$num_mental_ilnesses))
> summary(lm(Overall_work_impairment~A$num_mental_illnesses))
Call:
lm(formula = Overall_work_impairment ~ A$num_mental_ilnesses)
lm(formula = Overall_work_impairment ~ A$num_mental_illnesses)
Residuals:
Min 1Q Median 3Q Max
@ -727,7 +727,7 @@ Residuals:
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 25.036 2.007 12.475 <2e-16 ***
A$num_mental_ilnesses 14.089 1.454 9.688 <2e-16 ***
A$num_mental_illnesses 14.089 1.454 9.688 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
@ -736,10 +736,10 @@ Residual standard error: 26.45 on 269 degrees of freedom
Multiple R-squared: 0.2586, Adjusted R-squared: 0.2559
F-statistic: 93.85 on 1 and 269 DF, p-value: < 2.2e-16
> summary(lm(Overall_work_impairment~A$num_mental_ilnesses2))
> summary(lm(Overall_work_impairment~A$num_mental_illnesses2))
Call:
lm(formula = Overall_work_impairment ~ A$num_mental_ilnesses2)
lm(formula = Overall_work_impairment ~ A$num_mental_illnesses2)
Residuals:
Min 1Q Median 3Q Max
@ -748,7 +748,7 @@ Residuals:
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.4255 2.1752 7.551 6.73e-13 ***
A$num_mental_ilnesses2 12.1443 0.9577 12.681 < 2e-16 ***
A$num_mental_illnesses2 12.1443 0.9577 12.681 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
@ -757,10 +757,10 @@ Residual standard error: 24.31 on 269 degrees of freedom
Multiple R-squared: 0.3741, Adjusted R-squared: 0.3718
F-statistic: 160.8 on 1 and 269 DF, p-value: < 2.2e-16
> summary(lm(Overall_work_impairment~A$m_ill_or_not2 + A$num_mental_ilnesses2))
> summary(lm(Overall_work_impairment~A$m_ill_or_not2 + A$num_mental_illnesses2))
Call:
lm(formula = Overall_work_impairment ~ A$m_ill_or_not2 + A$num_mental_ilnesses2)
lm(formula = Overall_work_impairment ~ A$m_ill_or_not2 + A$num_mental_illnesses2)
Residuals:
Min 1Q Median 3Q Max
@ -770,7 +770,7 @@ Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.251 2.688 3.814 0.000170 ***
A$m_ill_or_not2 16.453 4.387 3.750 0.000216 ***
A$num_mental_ilnesses2 8.821 1.288 6.847 5.14e-11 ***
A$num_mental_illnesses2 8.821 1.288 6.847 5.14e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
@ -871,11 +871,11 @@ Index_of_access_mediated_by_mh -3.0173 0.4818 -6.263 1.5e-09 ***
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
```
That is, for respondents who have a mental health condition, having the worst possible score in the index of access, a -10, is correlated with losing -3*(-10) = 30 less productivity points, that is, perhaps being 30% less productive. Note that the minimum score was -9. On the other hand, having the best score in the index of access, a 10, is correlated with losing -3*10 = -30 productivity points, that is, perhaps being 30% more productive. That would be on top of a productivity loss of 42% for having a mental ilness in the first place.
That is, for respondents who have a mental health condition, having the worst possible score in the index of access, a -10, is correlated with losing -3*(-10) = 30 less productivity points, that is, perhaps being 30% less productive. Note that the minimum score was -9. On the other hand, having the best score in the index of access, a 10, is correlated with losing -3*10 = -30 productivity points, that is, perhaps being 30% more productive. That would be on top of a productivity loss of 42% for having a mental illness in the first place.
Repeating myself, I would take these numbers with a grain of salt, because I'm not sure respondents understood that an answer of 5 on a scale of 1-10 would be interpreted as a 50% reduction in effectiveness. If the survey is administered again, I'd prefer for the question to make a reference to percentages explicitly. And still I would be skeptical, because I don't expect people to estimate percentage of productivity lost with much precision. The reader is welcome to come to their own conclusions.
However, if taken at face value, these answers imply that the value of providing mental health ressources might be huge. More precisely, if the above is an upper bound, the upper bound is high enough for continued interest. A lower bound, modulo survey selection effects, is an average loss of 10% of work hours among the respondents, and a loss of 18% of work hours among those diagnosed with a mental condition.
However, if taken at face value, these answers imply that the value of providing mental health resources might be huge. More precisely, if the above is an upper bound, the upper bound is high enough for continued interest. A lower bound, modulo survey selection effects, is an average loss of 10% of work hours among the respondents, and a loss of 18% of work hours among those diagnosed with a mental condition.
```
> mean(Percent_missed_due_to_mental_health, na.rm=TRUE)
@ -890,9 +890,9 @@ However, if taken at face value, these answers imply that the value of providing
Using the same methodologies as above, the crossectional estimate of providing better information are also large. A productivity improvement of ~10% if information is very easy to come by, respectively a productivity loss of ~10% if finding it is very hard (~12% if restricting the regression to those with a diagnosis)
Alone in terms of work hours, 2 would be gain (resp. lost) every two weeks if responents find information very easy to come by (resp. very hard), amongst respondents who have been diagnosed with a mental ilness or think they have one (~3 hours if one only considers those with a diagnosis).
Alone in terms of work hours, 2 would be gain (resp. lost) every two weeks if responents find information very easy to come by (resp. very hard), amongst respondents who have been diagnosed with a mental illness or think they have one (~3 hours if one only considers those with a diagnosis).
I personally consider it likely that the effect is so large because the causal mechanism goes in both directions: a less burdensome mental ilness -> easier to do things like finding information, or not missing work hours, but also: information is easier to find -> condition gets better -> less work hours are missed.
I personally consider it likely that the effect is so large because the causal mechanism goes in both directions: a less burdensome mental illness -> easier to do things like finding information, or not missing work hours, but also: information is easier to find -> condition gets better -> less work hours are missed.
```
> i=17
@ -1041,9 +1041,9 @@ OWI_m_ill2 1.246629e-07 0.004133647 0.01294885 6.68103
```
### 10. How does access to mental health ressources vary with a wide variety of factors?
### 10. How does access to mental health resources vary with a wide variety of factors?
Acess is asked about in the following four questions:
Access is asked about in the following four questions:
14. How challenging was it to receive the mental healthcare services you needed within the past 12 months?
15. How challenging is it to find useful information on mental healthcare services?
@ -1053,7 +1053,7 @@ Acess is asked about in the following four questions:
The main limitation of these questions is that the answers will depend on the subjective experience and capabilities of the respondents.
I find questions 17 and 18 to be the most informative variants, and have used them to codify access to healthcare. I also see the later three questions to be logically disjunct, that is, one could in principle have found it very easy to get information, but be really unsatisfied with the mental healthcare one ends up receiving. However, running regressions, we see that they're all significantly correlated with each other (but there's still variability left). For example, if we correlate the first with the rest, after transforming verbal answers to a numerical scale, we get:
I find questions 17 and 18 to be the most informative variants, and have used them to codify access to healthcare. I also see the later three questions to be logically disjunctive, that is, one could in principle have found it very easy to get information, but be really unsatisfied with the mental healthcare one ends up receiving. However, running regressions, we see that they're all significantly correlated with each other (but there's still variability left). For example, if we correlate the first with the rest, after transforming verbal answers to a numerical scale, we get:
```
Call:
@ -1168,7 +1168,7 @@ Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’
```
#### 10.3. By mental health
Among all our markers, having worse mental health is correlated with finding it more challenging to receive mental healthcare, and to find information about it. Sometimes the effect is significant, p<0.05, but it it is never large (the largest is a change of -0.16 in a -3 to +3 scale). This also holds if we consider an index. This might be explained as follows: Once you have a mental ilness, getting (good) treatment for it doesn't mean you stop having it.
Among all our markers, having worse mental health is correlated with finding it more challenging to receive mental healthcare, and to find information about it. Sometimes the effect is significant, p<0.05, but it it is never large (the largest is a change of -0.16 in a -3 to +3 scale). This also holds if we consider an index. This might be explained as follows: Once you have a mental illness, getting (good) treatment for it doesn't mean you stop having it.
#### 10.4. By age
No effect. No effect with index, by which I mean that the coefficient is close to 0, and that the standard error is also small.
@ -1189,7 +1189,7 @@ Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’
```
If we disgreggate where this comes from, we see that the effect is coming from more involvement with EA being correlated EAs experiencing less financial difficulties because of mental ilness, and with being more satisfied with the healthcare they get. However, this is difficult to tell, because all the factors are correlated with each other.
If we disaggregate where this comes from, we see that the effect is coming from more involvement with EA being correlated EAs experiencing less financial difficulties because of mental illness, and with being more satisfied with the healthcare they get. However, this is difficult to tell, because all the factors are correlated with each other.
```
> summary(lm(Involvement_in_EA ~ I1 + I2 + I3 + I4 + I5))
@ -1211,26 +1211,26 @@ Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’
Some of the questions asked their respondents for their thoughts, and I really appreciated some of the long and insightful answers. Here, I paraphrase and expand on some of the key ideas and leave a technical comment for the footnotes [1]. I have found these comments very useful as way to think about the problem of mental health in EA overall.
#### 1.1. Selection effects in EA.
Some respondents suggested that EA attracts people with mental ilnesses. Perhaps there is a snowball effect going on, perhaps it selects from demographic which have higher rates of mental ilness. Thus, a particularly cost effective way to fight mental health in EA might be to do outreach amongst people who do not have mental health issues.
Some respondents suggested that EA attracts people with mental illnesses. Perhaps there is a snowball effect going on, perhaps it selects from demographic which have higher rates of mental illness. Thus, a particularly cost effective way to fight mental health in EA might be to do outreach amongst people who do not have mental health issues.
This model is consistent with finding out that, among effective altrists, more effective altruism is not correlated (or correlated weakly) with better mental health (see section 3.)
This model is consistent with finding out that, among effective altruists, more effective altruism is not correlated (or correlated weakly) with better mental health (see section 3.)
#### 1.2. Do mental health problems stem from EA-specific beliefs?
A respondent asked about how to deal with work-aholism when your work actually matters. Some people have talked with me about how, if ideas related to existential risk are internalized, Angst might occur. Thus, for EA specific problems, therapists familiar with EA ideas might help more than regular therapists.
A respondent asked about how to deal with workaholism when your work actually matters. Some people have talked with me about how, if ideas related to existential risk are internalized, Angst might occur. Thus, for EA specific problems, therapists familiar with EA ideas might help more than regular therapists.
What follows are my own thoughts.
This model is not is not consistent with finding out that, among effective altruists, more effective altruism is not correlated (or correlated weakly) with better mental health (see section 3). That is, if effective altruism caused mental ilness, we have lost part of the probability mass which comes from (more effective altruism -> more mental ilness). Instead, only the probability mass corresponding to (if a certain level of effective altruism is reached -> more mental ilness).
This model is not is not consistent with finding out that, among effective altruists, more effective altruism is not correlated (or correlated weakly) with better mental health (see section 3). That is, if effective altruism caused mental illness, we have lost part of the probability mass which comes from (more effective altruism -> more mental illness). Instead, only the probability mass corresponding to (if a certain level of effective altruism is reached -> more mental illness).
For a toy model, consider for example whether mental ilness is caused by involvement in effective altruism and mediated by understanding x-risk, that is, suppose that understanding x-risk led to (a chance of developing) depression/anxiety, and that higher levels of effective altruism led to higher chances of understanding x-risk. For example, suppose that numerical answers to "How involved are you in the EA community?", from 1 to 6 were such that: Answering 1 (not very involved) leads to a 10% probability of understanding x-risk, 2->20%, ..., 6-> 60%. Imagine then that our survey has serious selection effects (such that people with more mental ilness and people more familiar with effective altruism are more likely to participate). Then the effect would be amplified by these selection effects, and we *would* see a correlation between effective altruism and worse mental health.
For a toy model, consider for example whether mental illness is caused by involvement in effective altruism and mediated by understanding x-risk, that is, suppose that understanding x-risk led to (a chance of developing) depression/anxiety, and that higher levels of effective altruism led to higher chances of understanding x-risk. For example, suppose that numerical answers to "How involved are you in the EA community?", from 1 to 6 were such that: Answering 1 (not very involved) leads to a 10% probability of understanding x-risk, 2->20%, ..., 6-> 60%. Imagine then that our survey has serious selection effects (such that people with more mental illness and people more familiar with effective altruism are more likely to participate). Then the effect would be amplified by these selection effects, and we *would* see a correlation between effective altruism and worse mental health.
The fact that we *don't* is indicative of other models, like models with selection effects. For example, maybe obsessive thoughts relating to EA are exactly [like any other obsessive thoughts](https://slatestarcodex.com/2018/10/15/the-chamber-of-guf/). Maybe minds with mental conditions look for things to be depressed or anxious about, and effective altruism happens to provide some. Crucially, the counterfactual would not be not freaking out about stuff, it would be having fixated on something else to freak out about, like american politics, climate change, sexual assault, not being lovable, etc. *The content and origin of the idea being fixated on might be besides the point*. Under this model, EA therapists might be counterproductive.
#### 1.3. EA may not have a comparative advantage in providing mental health ressources.
#### 1.3. EA may not have a comparative advantage in providing mental health resources.
Either the market or other organizations, like universities or other NGOs specifically dedicated to mental health (CAMH, Zendo are mentioned, but I am not familiar with them).
#### 1.4. EA France has something going on
EA France has a group in which they read *Feeling Good*, by David Burns. I personally have benefitted from the book, and know that it's available on libgen (or a mirror, like b-ok.org).
EA France has a group in which they read *Feeling Good*, by David Burns. I personally have benefited from the book, and know that it's available on libgen (or a mirror, like b-ok.org).
Here is a formal invitation to EA France to talk about how the group is organized.
@ -1241,7 +1241,7 @@ A respondent notes that they think someone who has nothing to eat in a developin
Some people may join EA just to use these resources. Or some EAs who were paying for their therapy might choose to get it for free instead.
#### 1.7. Layers of indirectness and pathways to impact.
A respondent mentions that providing mental health ressources goes through two layers of indirectness: therapy may not help mental health, which may not help productivity. the comment stops here; what follows are my own thoughts.
A respondent mentions that providing mental health resources goes through two layers of indirectness: therapy may not help mental health, which may not help productivity. the comment stops here; what follows are my own thoughts.
Additionally, offering therapy does not mean that therapy is taken up, and an increase in productivity might not mean that the world will be made better.
@ -1253,12 +1253,12 @@ Thinking about this further, the case for providing EAs with mental healthcare s
With regards to pathway 1, [perhaps effective altruism are not the best demographic to worry about](https://forum.effectivealtruism.org/posts/XWSTBBH8gSjiaNiy7/cause-profile-mental-health).
With regards to pathway 2, we have a rough upwards crosssectional estimate of 2 hours of work saved per week if satisfactory mentalh healthcare is provided (with a more realistic estimate being 1 hour/week). If therapy lasts one hour per week, and the therapist has to be paid for one hour, and 10% of the counterfactual gain is donated, the math doesn't check out. However, this is only indicative, and one could argue that:
With regards to pathway 2, we have a rough upwards cross-sectional estimate of 2 hours of work saved per week if satisfactory mental healthcare is provided (with a more realistic estimate being 1 hour/week). If therapy lasts one hour per week, and the therapist has to be paid for one hour, and 10% of the counterfactual gain is donated, the math doesn't check out. However, this is only indicative, and one could argue that:
- The distribution is more important than the mean. That is, the average person does not exist; we may have a small number of people who could be a lot more effective if they had mental health, and a lot of people who wouldn't benefit that much (see image below)
- This requires to argue that filtering and organizational costs are not likely to be significant. I am skeptical of this if organized centrally, and less skeptical if local EA groups organize it themselves.
- Gains because of therapy continue after therapy has ended
- As opposed to regression to the mean? That is, the gains of therapy might not be people getting better, but people getting better sooner.
- People who recover from a mental ilness because of help from the EA community might not donate 10% of the counterfactual gain, but more.
- People who recover from a mental illness because of help from the EA community might not donate 10% of the counterfactual gain, but more.
- The gain is not only in the form of hours missed, but in productivity gained while working.
![](https://nunosempere.github.io/rat/eamentalhealth/analysis/Q7-9b.png)
@ -1276,26 +1276,26 @@ In each of the first three cases, there are many different steps in the process
- Mental healthcare works, and improves the patient's mental health somehow.
- In pathway 1, the process ends here.
- In pathway 2, better mental health leads to a degree of higher work efficiency / more work hours -> More donations to effective charities, f.ex., GiveDirectly -> Impact pathway of GiveDirectly.
- In pathway 3, better mental health -> higher likelihood of success -> pathway to impact of the effective project. The project presumably has to be at some point asessed.
- In pathway 3, better mental health -> higher likelihood of success -> pathway to impact of the effective project. The project presumably has to be at some point assessed.
All in all, althought the questions in our survey only ask about "offering mental health resources to effective altruists" in the abstract, the specific pathway to impact matters, because the several outlined here are different. In particular, if none of them work, being fuzzy about which one is in effect wouldn't help.
All in all, although the questions in our survey only ask about "offering mental health resources to effective altruists" in the abstract, the specific pathway to impact matters, because the several outlined here are different. In particular, if none of them work, being fuzzy about which one is in effect wouldn't help.
#### 1.8. A support group for EAs with ADHD
A commenter talked about forming a support group for EAs with ADHD. Here is a formal invitation to create one.
#### 1.9. Cheap ressources.
#### 1.9. Cheap resources.
Whereas therapists are relatively expensive, it's relatively cheap to make [Nate Soares' writing on guilt] (http://mindingourway.com/guilt/) more widely known. I personally have also recently gotten some value out of Kaj Sotala's blogposts on psychological frameworks (https://kajsotala.fi/blog/blog_english/); there is a certain power in hearing other people talk about their struggles with mental conditions.
SlateStarCodex's list of [mental health professionals](https://slatestarcodex.com/psychiat-list/), [ressources](https://www.reddit.com/r/raisedbynarcissists/comments/6cdmn2/new_here_helpful_posts_comments_from_rbnbestof/) by [r/raisedbynarcissists](https://www.reddit.com/r/raisedbynarcissists/wiki/helpfullinks), and in particular [this list of books for building your life](https://www.reddit.com/r/raisedbynarcissists/comments/1axuzu/book_list_for_building_your_life/), are free. I've personally gotten some value out of these [Strategies and tools for getting through a break up, from LessWrong](https://www.lesswrong.com/posts/opLKzAFQWCco8wQiH/strategies-and-tools-for-getting-through-a-break-up). Julia Wise seems to have [some](https://forum.effectivealtruism.org/posts/CJZGFxzHfdPuu2X76/a-mental-health-resource-for-ea-community) useful [things](https://forum.effectivealtruism.org/posts/ZGW8Tmc6mDWZTnqyo/burnout-and-self-care). The aforementioned *Feeling Good*, by David Burns is also free if found online (b-ok.org).
SlateStarCodex's list of [mental health professionals](https://slatestarcodex.com/psychiat-list/), [resources](https://www.reddit.com/r/raisedbynarcissists/comments/6cdmn2/new_here_helpful_posts_comments_from_rbnbestof/) by [r/raisedbynarcissists](https://www.reddit.com/r/raisedbynarcissists/wiki/helpfullinks), and in particular [this list of books for building your life](https://www.reddit.com/r/raisedbynarcissists/comments/1axuzu/book_list_for_building_your_life/), are free. I've personally gotten some value out of these [Strategies and tools for getting through a break up, from LessWrong](https://www.lesswrong.com/posts/opLKzAFQWCco8wQiH/strategies-and-tools-for-getting-through-a-break-up). Julia Wise seems to have [some](https://forum.effectivealtruism.org/posts/CJZGFxzHfdPuu2X76/a-mental-health-resource-for-ea-community) useful [things](https://forum.effectivealtruism.org/posts/ZGW8Tmc6mDWZTnqyo/burnout-and-self-care). The aforementioned *Feeling Good*, by David Burns is also free if found online (b-ok.org).
The point being that there are a lot of mental health ressources and information online, if only one knew where to find them, and >10% of survey respondents answered that finding information on mental health ressources was hard or very hard.
The point being that there are a lot of mental health resources and information online, if only one knew where to find them, and >10% of survey respondents answered that finding information on mental health resources was hard or very hard.
![](https://nunosempere.github.io/rat/eamentalhealth/Q15-b.png)
Thus, it is a high tractability, high neglectedness, small to medium impact task to create such a list of mental ressources for the EA community, even if collated and scavenged from other sources. If such a ressource already exist, some respondents do not seem to know about it.
Thus, it is a high tractability, high neglectedness, small to medium impact task to create such a list of mental resources for the EA community, even if collated and scavenged from other sources. If such a resource already exist, some respondents do not seem to know about it.
#### 1.10. Providing mental health ressources is creepy
In personal converations, a person in the outer orbit of the EA community has pointed out to me that providing mental health is creepy, and that they feel cringe when thinking about the idea. The word "cult" is mentioned. It is not implausible that providing mental health may put prospective EAs off.
#### 1.10. Providing mental health resources is creepy
In personal conversation, a person in the outer orbit of the EA community has pointed out to me that providing mental health is creepy, and that they feel cringe when thinking about the idea. The word "cult" is mentioned. It is not implausible that providing mental health may put prospective EAs off.
[1] [Technical note](https://concepts.effectivealtruism.org/concepts/information-hazards/): Let a be a variable which stands for an individual eas, and consider a mapping of O: A-> |N, such that O(a) falls in {1,...,n}, and consider a function like f(x) = c\*x^(-j)\*(1 + 1/sqrt(2\*pi\*9)\*exp(-x^2 / 2\*9}\*sin(x)/BB(6)), where BB is the busy beaver function. It may be that the counterfactual impact of eas follows such a distribution; j and c would be arbitrary constants, with j preferably greater than 3, because otherwise the variance is not well defined, and consider the relationship which the integral from 1 to k of f(x)dx and the integral from k+1 to n of f(x) dx have. It wouldn't be unsurprising if O(a) were not inversely correlated with conscientiousnes and initiative, and correlated, perhaps causally, with more mental health problems, as these variables often are. Now consider the first k such that the integral from 1 to k of f(x)dx > the integral from k+1 to n of f(x) dx. The question is now whether for high O(a), offering mental health is worth it, given that O(a) is a priori unknown, and that computing the exact value of f(O(a)) is arduous / subject to Goodhart's law or to moral hazards.
@ -1304,20 +1304,20 @@ In personal converations, a person in the outer orbit of the EA community has po
We first provided some shiny plots which summarized the results of the survey.
Then, we proceeded to analyze these results. We have seen that demographically, respondents varied somewhat compared with the 2018 EA Survey, but didn't worry too much about that. The rates of mental ilnesses were astoundingly high: 45% of respondents had been diagnosed with at least one mental condition, which shoots up to 71% if we include respondents who think they have a mental condition, but have not formally been diagnosed with one.
Then, we proceeded to analyze these results. We have seen that demographically, respondents varied somewhat compared with the 2018 EA Survey, but didn't worry too much about that. The rates of mental illnesses were astoundingly high: 45% of respondents had been diagnosed with at least one mental condition, which shoots up to 71% if we include respondents who think they have a mental condition, but have not formally been diagnosed with one.
This makes us suspect selection effects, but from the data, we cannot know whether these play out at the level of our survey, or at the level of the EA community. For this reason, we'd **strongly suggest to add a mental health section to the annual EA survey**. Nonetheless, some conclusions can be reached despite a potential bias, because sometimes the bias either doesn't matter, or we know its direction.
We also have the issue of p-hacking, or in general picking and choosing regressions to push a conclusion. Because computing power is cheap, we choose to instead run all the regressions, that is, we choose to operationalize our questions and to model our data in many different ways, and report the aggregate results. Additionally, most of our conclusions carry the caveat of coming from a crossectional analysis, a method which not only can be unreliable, but also doesn't tell us the direction of our effect. For example, if we run a regression GDP ~ education, we see that richer countries are more educated, but we can't say whether that is because they like to spend their money on education, or because being more educated makes them richer.
We also have the issue of p-hacking, or in general picking and choosing regressions to push a conclusion. Because computing power is cheap, we choose to instead run all the regressions, that is, we choose to operationalize our questions and to model our data in many different ways, and report the aggregate results. Additionally, most of our conclusions carry the caveat of coming from a cross-sectional analysis, a method which not only can be unreliable, but also doesn't tell us the direction of our effect. For example, if we run a regression GDP ~ education, we see that richer countries are more educated, but we can't say whether that is because they like to spend their money on education, or because being more educated makes them richer.
Nonetheless, our first conclusion is that, among the respondents of this survey, more involvement in the EA community is positively correlated with better mental health. The correlation does not reach any significance threshold whatsoever, and, we're relieved to find out that it doesn't go in the opposite direction. This is evidence against EA causing mental health problems, and a toy model is presented in which EA causes mental health through the idea of x-risk; we find that such a model is not consistent with our data.
As a proof of concept, we run a correlation on whether having mental health problems is correlated with thinking one could benefit from mental health ressources; we find the expected correlation, and get an absurdly low p-value. We also run a correlation on potential self-interested, that is, we ask whether thinking one could benefit from mental health ressources is correlated with answering that providing these ressources would be an effective thing to do. Again, we get a positive correlation with absurdly low p-values.
As a proof of concept, we run a correlation on whether having mental health problems is correlated with thinking one could benefit from mental health resources; we find the expected correlation, and get an absurdly low p-value. We also run a correlation on potential self-interested, that is, we ask whether thinking one could benefit from mental health resources is correlated with answering that providing these resources would be an effective thing to do. Again, we get a positive correlation with absurdly low p-values.
We then ask what the effect of mental health on productivity is, and how access to treatment mediates it. We see that people lose a lot of hours because of mental ilness: an additional diagnosed mental ilness is correlated with a loss of ~5 hours per 2 weeks. But conditions are highly comorbid, so being mentally ill is correlated with losing ~9 hours per 2 weeks. This is not homogeneusly distributed, but instead like a power law: a small proportion of respondents (~10%-20%) loose a lot of hours. Limiting our regression to that 10-20% takes our statistical power away, but nonetheless, knowing the shape of the distribution helps indicate what sort of interventions might be valuable.
We then ask what the effect of mental health on productivity is, and how access to treatment mediates it. We see that people lose a lot of hours because of mental illness: an additional diagnosed mental illness is correlated with a loss of ~5 hours per 2 weeks. But conditions are highly co-morbid, so being mentally ill is correlated with losing ~9 hours per 2 weeks. This is not homogeneously distributed, but instead like a power law: a small proportion of respondents (~10%-20%) loose a lot of hours. Limiting our regression to that 10-20% takes our statistical power away, but nonetheless, knowing the shape of the distribution helps indicate what sort of interventions might be valuable.
Segregating respondents by whether they have received satisfactory healthcare, we find that those who have loose ~1-2 hours of work less than those who have not, and we think that this is probably and upwards estimate. In total, among our respondents 1 758 hours of work were lost because of mental ilness in the two weeks previous to our survey, compared to 16 737 hours worked, and 1 899 hours missed because of other reasons.
Segregating respondents by whether they have received satisfactory healthcare, we find that those who have loose ~1-2 hours of work less than those who have not, and we think that this is probably and upwards estimate. In total, among our respondents 1 758 hours of work were lost because of mental illness in the two weeks previous to our survey, compared to 16 737 hours worked, and 1 899 hours missed because of other reasons.
We look at the impact on mental health on productivity a second time with a different modelization, this time using The Work Productivity and Impairment Scale, and get what I think are upwards estimates of productivity lost, and of productivity regainable with suitable mental health ressources. We have the problem that the scale assumes that a 6 on a scale of 1-10 corresponds to a productivity loss of 60%, and I'm not sure whether that's what respondents thought when answering the question. We also get a high crossectional estimate of the value of information, but this is likely to be too high because of issues with causal bidirectionality. For completeness' sake, we report how access is correlated with productivity for all our measures of access, hoping that this be an additional data-point when asessing potential interventions.
We look at the impact on mental health on productivity a second time with a different modelization, this time using The Work Productivity and Impairment Scale, and get what I think are upwards estimates of productivity lost, and of productivity regainable with suitable mental health resources. We have the problem that the scale assumes that a 6 on a scale of 1-10 corresponds to a productivity loss of 60%, and I'm not sure whether that's what respondents thought when answering the question. We also get a high cross-sectional estimate of the value of information, but this is likely to be too high because of issues with causal bidirectionality. For completeness' sake, we report how access is correlated with productivity for all our measures of access, hoping that this be an additional data-point when assessing potential interventions.
Several questions in our survey ask respondents for their personal opinions and insights, and some of the observations which they make are quite sharp. I present the ones which are likely to be useful, expand on some of them, and see whether the data gathered supports the hypothesis they propose. That section is likely to be accessible to the casual reader, nonetheless, here are some brief highlights: Many respondents seem to think that there are selection effects going on in EA. Others propose that EA itself causally leads to mental conditions, and I give some nondefinitive arguments to why that might not be the case, supported by the data at hand. I sketch several layers which providing mental health would have to go through before the world is positively changed, consider three different possible pathways to impact which providing mental health to effective altruists may have, and warn that if none of them work, being fuzzy about which ones are in effect wouldn't help. Many respondents suggest creating or scavenging mental health ressources, and I mention some which have been of use to me. EA France seems to have something going on with a book club for reading *Feeling Good*, by David Burns, and I extend them an invitation to talk about it.
Several questions in our survey ask respondents for their personal opinions and insights, and some of the observations which they make are quite sharp. I present the ones which are likely to be useful, expand on some of them, and see whether the data gathered supports the hypothesis they propose. That section is likely to be accessible to the casual reader, nonetheless, here are some brief highlights: Many respondents seem to think that there are selection effects going on in EA. Others propose that EA itself causally leads to mental conditions, and I give some nondefinitive arguments to why that might not be the case, supported by the data at hand. I sketch several layers which providing mental health would have to go through before the world is positively changed, consider three different possible pathways to impact which providing mental health to effective altruists may have, and warn that if none of them work, being fuzzy about which ones are in effect wouldn't help. Many respondents suggest creating or scavenging mental health resources, and I mention some which have been of use to me. EA France seems to have something going on with a book club for reading *Feeling Good*, by David Burns, and I extend them an invitation to talk about it.