Drug sample management in University of Montreal family medicine teaching units
Bibliographic record
Abstract
Abstract Objective To describe the management and distribution of drug samples in family medicine teaching units (FMUs). Design Cross-sectional descriptive study. Setting All 16 FMUs affiliated with the Department of Family Medicine and Emergency Medicine at the University of Montreal in Quebec. Participants Health care professionals (physicians, residents, pharmacists, and nurses) who manage (n = 22) and dispense (n = 294) drug samples in the FMUs. Methods Data were collected between February and March 2013 using 2 self-administered questionnaires completed by health care professionals who manage or dispense drug samples. The data were subjected to descriptive and bivariate analyses. Results The participation rate was 100.0% for staff who manage drug samples and 72.5% for those who dispense them. Of the 16 participating FMUs, 12 have drug sample cabinets. Eight of the FMUs have a written institutional policy governing the management of drug samples. Of the 76.2% of respondents who said they distributed samples, more than half did not know whether their institution had a policy. In 7 of 12 FMUs with drug sample cabinets, access to samples is not restricted to those authorized to prescribe medications. Cabinets are most often managed by nurses (9 of 12 FMUs). Only 4 of 12 FMUs take regular inventory of cabinet contents. The main reasons cited for dispensing samples were to help a patient financially and to test for tolerance and efficacy when initiating or modifying a treatment for a patient. Three-quarters (78.2%) of dispensers reported that sometimes they were unable to find the drug they wanted in the cabinet; half of those consequently gave patients drugs that were not their first choice. More than half the dispensers reported they never or only occasionally referred patients to their community pharmacists. Conclusion A portrait of drug sample management and dispensation in the academic FMUs emerged from this study. This study provides insight into current practice and lays the groundwork for the development of guidelines for safe and ethical handling of drug samples.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".