Healthcare consumers’ perceptions of incentive-linked prescribing: A scoping review
Bibliographic record
Abstract
Incentive-linked prescribing (ILP) is considered a controversial practice universally. If incentivised, physicians may prioritise meeting pharmaceutical sales targets through prescriptions, rather than considering patients' health and wellbeing. Despite the potential harms of ILP to patients and important stakeholders in the healthcare system, healthcare consumers (HCCs) which include patients and the general public often have far less awareness about the practice of pharmaceutical incentivisation of physicians. We conducted a scoping review to explore what existing research says about HCCs' perceptions of the financial relationship between physicians and pharmaceutical companies. To conduct this scoping review, we followed Arksey and O'Malley's five-stage framework: identifying research questions, identifying relevant studies, selecting eligible studies, data charting, and collating, summarising, and reporting results. We also used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses' extension for scoping reviews (PRISMA-ScR), as a guide to organise the information in this review. Quantitative and qualitative studies with patients and the general public, published in the English language were identified through searches of Scopus, Medline (OVID), EMBASE (OVID), and Google Scholar. Three themes emerged through the analysis of the 13 eligible studies: understanding of incentivisation, perceptions of hazards linked to ILP, and HCCs' suggestions to address it. We found documentation that HCCs exhibited a range of knowledge from good to insufficient about the pharmaceutical incentivisation of physicians. HCCs perceived several hazards linked to ILP such as a lack of trust in physicians and the healthcare system, the prescribing of unnecessary medications, and the negative effect on physicians' reputations in society. In addition to strong regulatory controls, it is critical that physicians self-regulate their behaviour, and publicly disclose if they have any financial ties with pharmaceutical companies. Doing so can contribute to trust between patients and physicians, an important part of patient-focused care and a contributor to user confidence in the wider health system.
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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 | 0.043 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".