Trends in Psychotropic Use in Saskatchewan from 1983 to 2007
Bibliographic record
Abstract
OBJECTIVE: There has been little research reported on trends in the use of a full spectrum of psychotropics in a general population. We provide an overview of trends in psychotropic use over a 24-year period for Saskatchewan. METHODS: Data were drawn from the Saskatchewan Ministry of Health administrative data files. It covers antidepressants (ADs), antipsychotics, mood stabilizers, anxiolytics, stimulants, and cholinesterase inhibitors in outpatient settings. We analyzed data from 9 triennial years from 1983 to 2007. Descriptive statistics were used. RESULTS: Among the Saskatchewan population in our study, 8.38% were prescribed at least 1 psychotropic in 1983. The prevalence decreased to 7.44% in 1989, then gradually increased to 12.90% in 2007. We found a continuous increase in the number of psychotropic prescriptions for both males and females. The trend became more marked during the 1990s. Females used more psychotropics. Family physicians were the major prescribers, and their prescriptions dramatically increased over the period. There was an increase in the prescribing of all psychotropics except for anxiolytics. AD prescriptions dramatically increased, especially from 1995 onward. The proportion of patients with 8 to 11 and 12 or more prescriptions per year also gradually increased, whereas the proportion of patients with less than 3 prescriptions per year decreased. CONCLUSIONS: AD prescriptions are the major reason for the increasing trend of psychotropic use. Given the major role of family physicians in the use of psychotropics, the need for appropriate training and continuing education is reinforced.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".