Drug samples in family medicine teaching units: a cross-sectional descriptive study: Part 1: drug sample management policies and the relationship between the pharmaceutical industry and residents in Quebec.
Bibliographic record
Abstract
OBJECTIVE: To determine the existence and the level of health care professional (HCP) knowledge of local policies regarding drug sample use and the relationship between residents and the pharmaceutical industry in academic primary health care settings. DESIGN: Descriptive cross-sectional survey. Health care providers were invited to complete a self-administered questionnaire on drug sample use between February and December 2013. Managers of drug samples were also asked to complete a specific questionnaire on drug sample management and policies and an inventory log sheet. Data about the existence of written policies were validated with health and social services centre (HSCC) directors or pharmacy departments and family medicine teaching unit (FMTU) directors between February and June 2014. SETTING: All 42 FMTUs in Quebec. PARTICIPANTS: were defined as HCPs or staff members who managed drug samples. MAIN OUTCOME MEASURES: Existence of written policies on drug sample use in HSCCs and FMTUs; whether FMTUs applied the HSCC policies if they existed; whether dispensers were aware of the existence of the policies; and whether policies on the relationships between residents and pharmaceutical companies existed. RESULTS: Among the 42 FMTUs, 33 (79%) kept drug samples. Of these, 30% (10 of 33) did not have policies about drug samples in the FMTU or in the HSCC. A total of 67% (579 of 859) of HCPs from these FMTUs reported using drug samples. Most dispensers did not know if a policy existed in their FMTU (n = 297; 51%) or their HSCC (n = 420; 73%). Eleven (26%) of the 42 FMTU directors reported having a policy regarding relationships between residents and the pharmaceutical industry. Most drug sample dispensers were not aware whether such a policy existed (n = 310; 54%). CONCLUSION: Many FMTUs did not have policies regarding drug samples or relationships between residents and the pharmaceutical industry. Variation in use and management of drug samples and the lack of knowledge of HCPs about the existence of policies point to the need to implement uniform policies in all FMTUs in Quebec.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".