Interprovincial Variation of Psychotropic Prescriptions Dispensed to Older Canadian Adults
Bibliographic record
Abstract
BACKGROUND: Utilization of psychotropic medications among the elderly has garnered attention due to concerns about safety and degree of efficacy, but may be used differently across regions. METHODS: We conducted a cross-sectional study of all antipsychotic, benzodiazepine, and trazodone prescriptions dispensed to seniors ( ≥ 65 years) leveraging IQVIA (Durham, NC) GPM data in 2013. We report the units dispensed (per 100 seniors) by province. RESULTS: Nationally, on average, 26,210 units of antipsychotics, 24,257 of benzodiazepines, and 7,519 of trazodone were dispensed in 2013 for every 100 seniors; reports varied across Canada. The rate of antipsychotic and benzodiazepine prescribing was highest in New Brunswick (AP: 35,375 units per 100, BZD: 43,989 units per 100), and lowest in Newfoundland & Labrador for antipsychotics (20,974 per 100) and Saskatchewan for benzodiazepines (12,692 per 100). Trazodone unit dispensation rates were highest in Nova Scotia (9,164 per 100) and lowest in Newfoundland & Labrador (2,968 per 100). CONCLUSIONS: There is considerable geographic variation in the prescribing patterns of antipsychotics, benzodiazepine, and trazodone. This study serves as the first step in understanding these differences, while future work is needed to develop region-specific strategies to optimize the prescribing of psychotropic medications to older Canadian adults.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".