Ethical Shades of Gray: Questionable Research Practices in Health Professions Education
Bibliographic record
Abstract
Abstract Purpose To maintain scientific integrity and engender public confidence, research must be conducted responsibly. Whereas scientific misconduct, like data fabrication, is clearly irresponsible and unethical, other behaviors—often referred to as questionable research practices (QRPs)—exploit the ethical shades of gray that color acceptable practice. This study aimed to measure the frequency of self-reported QRPs in a diverse, international sample of health professions education (HPE) researchers. Method In 2017, the authors conducted an anonymous, cross-sectional survey study. The web-based survey contained 43 QRP items that asked respondents to rate how often they had engaged in various forms of scientific misconduct. The items were adapted from two previously published surveys. Results In total, 590 HPE researchers took the survey. The mean age was 46 years (SD=11.6), and the majority of participants were from the United States (26.4%), Europe (23.2%), and Canada (15.3%). The three most frequently reported QRPs were adding authors to a paper who did not qualify for authorship (60.6%), citing articles that were not read (49.5%), and selectively citing papers to please editors or reviewers (49.4%). Additionally, respondents reported misrepresenting a participant’s words (6.7%), plagiarizing (5.5%), inappropriately modifying results (5.3%), deleting data without disclosure (3.4%), and fabricating data (2.4%). Overall, 533 (90.3%) respondents reported at least one QRP. Conclusions Notwithstanding the methodological limitations of survey research, these findings indicate that a substantial proportion of HPE researchers report a range of QRPs. In light of these results, reforms are needed to improve the credibility and integrity of the HPE research enterprise. “Researchers should practice research responsibly. Unfortunately, some do not.” –Nicholas H. Steneck, 2006 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrity Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | MetaresearchResearch integrity Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.026 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".