Knowledge, Beliefs and Attitudes of Patients and the General Public towards the Interactions of Physicians with the Pharmaceutical and the Device Industry: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To systematically review the evidence on the knowledge, beliefs, and attitudes of patients and the general public towards the interactions of physicians with the pharmaceutical and the device industry. METHODS: We included quantitative and qualitative studies addressing any type of interactions between physicians and the industry. We searched MEDLINE and EMBASE in August 2015. Two reviewers independently completed data selection, data extraction and assessment of methodological features. We summarized the findings narratively stratified by type of interaction, outcome and country. RESULTS: Of the 11,902 identified citations, 20 studies met the eligibility criteria. Many studies failed to meet safeguards for protecting from bias. In studies focusing on physicians and the pharmaceutical industry, the percentages of participants reporting awareness was higher for office-use gifts relative to personal gifts. Also, participants were more accepting of educational and office-use gifts compared to personal gifts. The findings were heterogeneous for the perceived effects of physician-industry interactions on prescribing behavior, quality and cost of care. Generally, participants supported physicians' disclosure of interactions through easy-to-read printed documents and verbally. In studies focusing on surgeons and device manufacturers, the majority of patients felt their care would improve or not be affected if surgeons interacted with the device industry. Also, they felt surgeons would make the best choices for their health, regardless of financial relationship with the industry. Participants generally supported regulation of surgeon-industry interactions, preferably through professional rather than governmental bodies. CONCLUSION: The awareness of participants was low for physicians' receipt of personal gifts. Participants also reported greater acceptability and fewer perceived influence for office-use gifts compared to personal gifts. Overall, there appears to be lower awareness, less concern and more acceptance of surgeon-device industry interactions relative to physician-pharmaceutical industry interactions. We discuss the implications of the findings at the patient, provider, organizational, and systems level.
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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 | 0.014 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".