Development of a Typology of Antidepressant Users: The Role of Mental Health Disorders and Substance Use.
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Antidepressants constitute one of the most consumed classes of psychotropic medication. This study aims to identify a typology of users based on their individual characteristics, such as presence of a mental disorder and use of other psychotropic medication during the last year. METHODS: Antidepressant use for residents in the epidemiological zone of South-West Montreal aged 15 years and older was documented in 2009, 2011, and 2013. Among the 2433 participants from the initial study, 249 had used antidepressants (10%). A cluster analysis, validated with Chi-square tests and Cramer's V measure, was conducted with this sample. The longitudinal profile of the clusters was examined using curve clustering. RESULTS: Based on clinical variables measured at Time 1, four types of antidepressant users were identified (p<0,001; 0,58 ≤ V ≤ 0,81): depressed users without anxiety (15%), anxio-depressive users with substance dependence and polypharmacy (26%), depressed users with polypharmacy and being treated by a psychiatrist (31%) and users without mental health disorders (28%). Follow-ups two and four years later indicate a higher proportion of participants with symptoms of depression persisting over time within the cohort of persons having concurrent anxiety and substance dependence. CONCLUSIONS: Results help improve knowledge about the context of antidepressant use in order to plan appropriate interventions. Depressed persons without anxiety appear to receive treatment from general practitioners, while those with comorbid psychotic disorders and depression may require specialized treatment from a psychiatrist. Anxio-depressive users with substance dependence and polypharmacy may require integrated services from different specialized networks in order to counter symptoms related to comorbidity.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".