Legislative, educational, policy and other interventions targeting physicians’ interaction with pharmaceutical companies: a systematic review
Bibliographic record
Abstract
BACKGROUND: Pharmaceutical company representatives likely influence the prescribing habits and professional behaviour of physicians. OBJECTIVE: The objective of this study was to systematically review the effects of interventions targeting practising physicians' interactions with pharmaceutical companies. ELIGIBILITY CRITERIA: We included observational studies, non-randomised controlled trials (non-RCTs) and RCTs evaluating legislative, educational, policy or other interventions targeting the interactions between physicians and pharmaceutical companies. DATA SOURCES: The search strategy included an electronic search of MEDLINE and EMBASE. Two reviewers performed duplicate and independent study selection, data abstraction and assessment of risk of bias. APPRAISAL AND SYNTHESIS METHODS: We assessed the risk of bias in each included study. We summarised the findings narratively because the nature of the data did not allow a meta-analysis to be conducted. We assessed the quality of evidence by outcome using the GRADE methodology. RESULTS: Of 11 189 identified citations, one RCT and three observational studies met the eligibility criteria. All four studies specifically targeted one type of interaction with pharmaceutical companies, that is, interactions with drug representatives. The RCT provided moderate quality evidence of no effect of a 'collaborative approach' between the pharmaceutical industry and a health authority. The three observational studies provided low quality evidence suggesting a positive effect of policies aiming to reduce interaction between physicians and pharmaceutical companies (by restricting free samples, promotional material, and meetings with pharmaceutical company representatives) on prescription behaviour. LIMITATIONS: We identified too few studies to allow strong conclusions. CONCLUSIONS: Available evidence suggests a potential impact of policies aiming to reduce interaction between physicians and drug representatives on physicians' prescription behaviour. We found no evidence concerning interventions affecting other types of interaction with pharmaceutical companies.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".