The Association of Treatment of Depressive Episodes and Work Productivity
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".