Research culture influences in health and biomedical research: rapid scoping review and content analysis
Bibliographic record
Abstract
BACKGROUND: Research culture is strongly influenced by academic incentives and pressures such as the imperative to publish in academic journals, and can influence the nature and quality of the evidence we produce. OBJECTIVE: The purpose of this rapid scoping review is to capture the breadth of differential pressures and contributors to current research culture, drawing together content from empirical research specific to the health and biomedical sciences. STUDY DESIGN AND SETTING: PubMed and Web of Science were searched for empirical studies of influences and impacts on health and biomedical research culture, published between January 2012 and April 2024. Data charting extracted the key findings and relationships in research culture from included papers such as workforce composition; equitable access to research; academic journal trends, incentives, and reproducibility; erroneous research; questionable research practices; biases vested interests; and misconduct. A diverse author network was consulted to ensure content validity of the proposed framework of i) inclusivity, ii) transparency, iii) rigor, and iv) objectivity. RESULTS: A growing field of studies examining research culture exists ranging from the inclusivity of the scientific workforce, the transparency of the data generated, the rigor of the methods used and the objectivity of the researchers involved. Figurative diagrams are presented to storyboard the links between research culture content and findings. CONCLUSION: The wide range of research culture influences in the recent literature indicates the need for coordinated and sustained research culture conversations. Core principles in effective research environments should include inclusive collaboration and diverse research workforces, rigorous methodological approaches, transparency, data sharing, and reflection on scientific objectivity.
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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 | 0.445 | 0.669 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.075 | 0.058 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".