Honest yet unacceptable research practices: when research becomes a health risk
Bibliographic record
Abstract
BACKGROUND: Examples of poor research practices have received much attention in academic and public arenas. Such practices persist and threaten the health of the public and the reputation and impact of research and researchers. OBJECTIVE: In this article, we argue that research-though intended to improve health-can lead to patient harm through the proliferation of honest (though occasionally dishonest) yet unacceptable research practices. SUMMARY OF KEY ARGUMENTS: We argue that deliberate dishonest research practices-termed questionable research practices-are widely prevalent and insidious and influenced by both individual and cultural factors. Drawing on credible conceptualisations of poor research practices, we define honest yet unacceptable research practices to be different from questionable research practices involving dishonesty, but just as serious due to their wide prevalence and damaging impacts. Finally, we present recommendations for people and organisations to better protect patients' interests from honest yet unacceptable research practices. CONCLUSION: Our recommendations can serve as the basis for preventing honest yet unacceptable poor research practices to safeguard public trust in health professions, researchers and practices.
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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 | 0.167 | 0.388 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.012 | 0.013 |
| 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 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".