Drug samples in family medicine teaching units: a cross-sectional descriptive study: Part 3: availability and use of drug samples in Quebec.
Bibliographic record
Abstract
OBJECTIVE: To draw a portrait of drug sample distribution and to assess the concordance between drug samples distributed and the medical problems encountered in the ambulatory primary health care setting. DESIGN: were defined as HCPs reporting the use of drug samples. Concurrently, an inventory log sheet was completed by managers of drug samples to document the contents of sample cabinets. Data from the Canadian Disease and Therapeutic Index were used as the criterion standard to assess the consistency between the drug samples found in the cabinets and the profile of the most frequent health problems encountered in primary care. SETTING: All 33 FMTUs that kept drug samples in Quebec. PARTICIPANTS: Health care professionals authorized to hand out drug samples (practising physicians, residents, pharmacists, and nurses), and managers of drug sample cabinets. MAIN OUTCOME MEASURES: Dispensing practices of HCPs; number of doses of each drug contained in the sample cabinets; total market value of the samples; concordance between the drug sample categories made available and the most common medical problems encountered in primary care; and data on safe handling, ethical issues, effect of the pharmaceutical industry on prescribing behaviour, and inventory of samples. RESULTS: Among 859 HCPs, 579 (67%) reported dispensing drug samples. A large proportion of dispensers (88%) were unable to find the specific drug they sought and half of them (51%) provided the patients with a drug sample even if it was not their first choice for treatment. The drug sample cabinet inventory revealed products from 292 different companies and identified a total of 382 363 medication doses for a total value of $201 872. We found gaps among types of drugs provided to patients, those the HCPs would consider useful, and those available in the cabinets. CONCLUSION: Drug samples available in FMTUs do not meet the needs of many patients and HCPs, suggesting that the main driving force for drug sample distribution is not patient care. Policies on drug samples in FMTUs should be uniform across the province, and management should be as strict as in community pharmacies. Otherwise, prohibiting their use should be considered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".