Psychotropic Drug Prescribing Survey in a Canadian Rehabilitation and Complex Care Facility
Bibliographic record
Abstract
OBJECTIVE: To describe rates of inpatient prescribing of psychotropic drugs in a rehabilitation and complex continuing care setting. DESIGN: Cross-sectional, observational study. SETTING: Providence Healthcare, Toronto, Ontario, Canada. PATIENTS: Inpatients registered in the hospital on each of four annual audit dates. INTERVENTION: An audit of medication profiles for the presence of psychotropic prescriptions, done yearly on a single day in May 2007, 2008, 2010, and 2011. MAIN OUTCOME MEASURES: The percentage of inpatients prescribed at least one antidepressant, antipsychotic, benzodiazepine, or zopiclone. RESULTS: The percentage of inpatients with at least one prescription for each class of psychotropic drug (ranging from the lowest to highest audit-year results) were as follows: any psychotropic (55% to 63%), benzodiazepines or zopiclone (31% to 40%), antidepressants (24% to 32%), antipsychotics (7% to 13%). Rates of polypharmacy within classes was highest with antidepressants, followed by benzodiazepines (including zopiclone), then antipsychotics. CONCLUSION: Despite the limitations associated with cross-sectional, observational data, rates of prescribing of psychotropic medication, apart from antipsychotics, were high. Future research will be performed to assess appropriateness of prescribing and adverse events.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".