Use of Antidepressants Among Canadian Workers Receiving Depression-Related Short-Term Disability Benefits
Bibliographic record
Abstract
OBJECTIVES: Little is known about how antidepressants are being used, but rising antidepressant expenditures and the accompanying impulse to control costs make this a critical issue to be addressed. The authors studied patterns of antidepressant use in a population of workers receiving depression-related short-term disability benefits to determine whether populations likely to benefit from antidepressants are using them and, if so, whether they are using them in a way that the benefits from their use are maximized. METHODS: The analyses were based on 1996-1998 administrative data from short-term disability and prescription drug benefit claims and occupational health department records for employees of three Canadian companies. RESULTS: Approximately 58 percent of employees who were receiving depression-related short-term disability benefits had made at least one antidepressant claim. Employees who did not use antidepressants typically reported significantly fewer symptoms at baseline on average than those who did. About 91 percent of the employees who used antidepressants filled at least one prescription for a guideline-recommended first-line agent. Approximately 79 percent of antidepressant dosages reflected those suggested by the Canadian Network for Mood and Anxiety Treatment, and three timeframe indicators suggested that most patients used antidepressants within the recommended timeframes. CONCLUSIONS: The results of this study represent an important first step in exploring the question of how antidepressants are used among workers with depression-related disability. For the most part, these workers and those whose depression was more severe were more likely to obtain antidepressants.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".