Attitudes and practices of public health academics towards research funding from for-profit organizations: cross-sectional survey
Bibliographic record
Abstract
OBJECTIVES: The growing trend of for-profit organization (FPO)-funded university research is concerning because resultant potential conflicts of interest might lead to biases in methods, results, and interpretation. For public health academic programmes, receiving funds from FPOs whose products have negative health implications may be particularly problematic. METHODS: A cross-sectional survey assessed attitudes and practices of public health academics towards accepting funding from FPOs. The sampling frame included universities in five world regions offering a graduate degree in public health; 166 academics responded. Descriptive, bivariate, and logistic regression analyses were conducted. RESULTS: Over half of respondents were in favour of accepting funding from FPOs; attitudes differed by world region and gender but not by rank, contract status, % salary offset required, primary identity, or exposure to an ethics course. In the last 5 years, almost 20% of respondents had received funding from a FPO. Sixty per cent of respondents agreed that there was potential for bias in seven aspects of the research process, when funds were from FPOs. CONCLUSIONS: Globally, public health academics should increase dialogue around the potential harms of research and practice funded by FPOs.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".