The Association of Treatment of Depressive Episodes and Work Productivity
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: About one-third of the annual $51 billion cost of mental illnesses is related to productivity losses. However, few studies have examined the association of treatment and productivity. The purpose of our research is to examine the association of depression and its treatment and work productivity. METHODS: Our analyses used data from 2737 adults aged between 18 and 65 years who participated in a large-scale community survey of employed and recently employed people in Alberta. Using the World Health Organization's Health and Work Performance Questionnaire, a productivity variable was created to capture high productivity (above the 75th percentile). We used regression methods to examine the association of mental disorders and their treatment and productivity, controlling for demographic factors and job characteristics. RESULTS: In the sample, about 8.5% experienced a depressive episode in the past year. The regression results indicated that people who had a severe depressive episode were significantly less likely to be highly productive. Compared with people who had a moderate or severe depressive episode who did not have treatment, those who did have treatment were significantly more likely to be highly productive. However, about one-half of workers with a moderate or severe depressive episode did not receive treatment. CONCLUSIONS: Our results corroborate those in the literature that indicate mental disorders are significantly associated with decreased work productivity. In addition, these findings indicate that treatment for these disorders is significantly associated with productivity. Our results also highlight the low proportion of workers with a mental disorder who receive treatment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it