Doctors taking bribes from pharmaceutical companies is common and not substantially reduced by an educational intervention: a pragmatic randomised controlled trial in Pakistan
Bibliographic record
Abstract
INTRODUCTION: Incentive-linked prescribing, which is when healthcare providers accept incentives from pharmaceutical companies for prescribing promoted medicines, is a form of bribery that harms patients and health systems globally. We developed a novel method using data collectors posing as pharmaceutical company sales representatives to evaluate private doctors' engagement in incentive-linked prescribing and the impact of a multifaceted educational intervention on reducing this practice in Karachi, Pakistan. METHODS: We made a sampling frame of all doctors running for-profit, primary-care clinics and randomly allocated participants to control and intervention groups (1:1). The intervention group received a multifaceted seminar on ethical prescribing and reinforcement messages over 6 weeks. The control group attended a seminar without mention of ethical prescribing. The primary outcome was the proportion of participants agreeing to accept incentives in exchange for prescribing promoted medicines from data collectors posing as pharmaceutical company representatives, 3 months after the seminars. RESULTS: We enrolled 419 of 440 eligible participants. Of 210 participants randomly allocated to the intervention group, 135 (64%) attended the intervention seminar and of 209 participants allocated to the control group, 132 (63%) attended the placebo seminar. The primary outcome was assessed in 130 (96%) and 124 (94%) of intervention and control participants, respectively. No participants detected the covert data collectors. 52 control group doctors (41.9%) agreed to accept incentives as compared with 42 intervention group doctors (32.3%). After adjusting for doctors' age, sex and clinic district, there was no evidence of the intervention's impact on the primary outcome (OR 0.70 [95% CI 0.40 to 1.20], p=0.192). CONCLUSIONS: This first study to covertly assess deal-making between doctors and pharmaceutical company representatives demonstrated that the practice is strikingly widespread in the study setting and suggested that substantial reductions are unlikely to be achieved by educational interventions alone. Our novel method provides an opportunity to generate evidence on deal-making between doctors and pharmaceutical companies elsewhere.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".