Lessons Learned From Trends in Psychotropic Drug Expenditures in a Canadian Province
Bibliographic record
Abstract
Although prescription drug prices are lower in Canada than in the United States, trends indicate that there has nevertheless been a steep increase in expenditures on psychotropic drugs. Between 1992 and 1998, such expenditures increased by 216 percent; 61 percent of these expenditures were on antidepressants, 33 percent on antipsychotics, and less than 7 percent on anxiolytics. Most of the increase in costs in Canada is attributable to a greater use of newer agents and the higher prices of these agents. These trends are a reminder not only that the use of newer, more expensive psychotherapeutic agents has become a widely embraced part of care but also that lower drug prices do not necessarily insulate a health care system from rising expenditures. The authors' findings prompt the questions of whether the use of these newer agents meets practice guidelines and whether there are ways to control the increases in drug expenditures while ensuring high-quality care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".