Quel est le rôle du travail dans l’incidence de la consommation de médicaments psychotropes au Canada?
Bibliographic record
Abstract
Objectives: This study aims to examine the contribution of work and other social determinants to the onset of psychotropic drug use among workers over an 8-year period. Methods: The study is based on a secondary analysis of the longitudinal data of the National Population Health Survey (NPHS) of Statistics Canada carried out between 1994-1995 and 2002-2003. A panel of 7,338 people aged 15 to 55 and employed at cycle 1 was selected. To establish the incidence rate, we included those participants identified at cycle 1 as not using psychotropic drugs. Overall, 7,020 people in 1,347 local communities did not use psychotropic drugs at cycle 1 and constituted the group at risk in the study. Discrete time survival multilevel regression models were used. Results: The onset of psychotropic drug use was estimated at 3.5% over the 8-year period studied. With the exception of the number of hours worked, occupations and other work characteristics measured in the NPHS do not show a significant contribution. Being a woman, age, physical health, smoking and stressful childhood events support an increased risk of psychotropic drug use, whereas certain personality traits decreased the risk of psychotropic drug use. Conclusions: The work factors measured in the NPHS seem to play a limited role in the incidence of psychotropic drug use. More research is needed to better capture patterns of workers’ psychotropic drug use over time. Key words: Psychotropic drugs; occupation; organizational work conditions; non-work factors; individual factors; longitudinal studies; multilevel analysis
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".