Facteurs associés à l’exposition élevée aux médicaments psychotropes identifiés lors d’une étude épidémiologique montréalaise
Bibliographic record
Abstract
This article examine the associations between elevated exposure to psychotropic medications, presence of a diagnosis of mental illness, and sociodemographic characteristics in the adult population. A questionnaire was administered by interview to 2433 individuals aged 15 and over in the epidemiological catchment area of South-West Montreal. The determinants of psychotropic medication consumption were analyzed using bivariate analysis and multivariate logistic regression. A significant association was observed between the consumption of sleeping medications, anxiolytics, and antidepressants and being older, female, living alone, having a low level of education and income, being unemployed during the 12 months preceding the study, and presence of a mental disorder. An elevated exposure to different psychotropic medications (three or more) was reported in 3.1% of the respondants. All things being equal, this increased with age, living alone, being unemployed over the course of the last year, and presence of a mental illness. The results suggest that it is necessary to consider social isolation and prevalence of mental illness in order to contextualize the elevated exposure to psychotropic medication. Polypharmacy may indeed pose important risks if it does not follow the logic of a coherent clinical intervention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".