Reasons for antidepressant prescriptions in Canada
Bibliographic record
Abstract
PURPOSE: To describe reasons reported by physicians making recommendations for treatment with antidepressant medications. METHODS: Data collected by IMS Health Canada in a database called the Canadian Disease and Therapeutic Index (CDTI) were used in this analysis. CDTI data are collected from a representative sample of office-based physicians who complete diaries in their practices during selected sampling periods. A drug recommendation is recorded each time a treatment is recommended. The data are weighted to produce national estimates of the frequency of such recommendations. RESULTS: The frequency of recommendations for antidepressant treatment increased between 2000 and 2004. However, there was a slight decrease in 2005. Two types of antidepressant medications, tricyclic antidepressants (TCAs) and trazodone showed distinct patterns of use. TCAs were more commonly used for non-psychiatric indications than for psychiatric indications, especially for sleep- and pain-related reasons. Trazodone was frequently recommended for sleep problems. The proportion of recommendations for depressive disorders for antidepressants as a group remained stable over the 5-year study period. CONCLUSIONS: About one-third of antidepressant recommendations are for reasons other than depression. It can no longer be assumed that the frequency of antidepressant use is a measure of the frequency of pharmacological depression treatment. However, prescription data may be useful for tracking trends.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".