The Influence of Industry Sponsorship on the Research Agenda: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Corporate interests have the potential to influence public debate and policymaking by influencing the research agenda, namely the initial step in conducting research, in which the purpose of the study is defined and the questions are framed. OBJECTIVES: We conducted a scoping review to identify and synthesize studies that explored the influence of industry sponsorship on research agendas across different fields. SEARCH METHODS: We searched MEDLINE, Scopus, and Embase (from inception to September 2017) for all original research and systematic reviews addressing corporate influence on the research agenda. We hand searched the reference lists of included studies and contacted experts in the field to identify additional studies. SELECTION CRITERIA: We included empirical articles and systematic reviews that explored industry sponsorship of research and its influence on research agendas in any field. There were no restrictions on study design, language, or outcomes measured. We excluded editorials, letters, and commentaries as well as articles that exclusively focused on the influence of industry sponsorship on other phases of research such as methods, results, and conclusions or if industry sponsorship was not reported separately from other funding sources. DATA COLLECTION AND ANALYSIS: At least 2 authors independently screened and then extracted any quantitative or qualitative data from each study. We grouped studies thematically for descriptive analysis by design and outcome reported. We developed the themes inductively until all studies were accounted for. Two investigators independently rated the level of evidence of the included studies using the Oxford Centre for Evidence-Based Medicine ratings. MAIN RESULTS: We included 36 articles. Nineteen cross-sectional studies quantitatively analyzed patterns in research topics by sponsorship and showed that industry tends to prioritize lines of inquiry that focus on products, processes, or activities that can be commercialized. Seven studies analyzed internal industry documents and provided insight on the strategies the industry used to reshape entire fields of research through the prioritization of topics that supported its policy and legal positions. Ten studies used surveys and interviews to explore the researchers' experiences and perceptions of the influence of industry funding on research agendas, showing that they were generally aware of the risk that sponsorship could influence the choice of research priorities. CONCLUSIONS: Corporate interests can drive research agendas away from questions that are the most relevant for public health. Strategies to counteract corporate influence on the research agenda are needed, including heightened disclosure of funding sources and conflicts of interest in published articles to allow an assessment of commercial biases. We also recommend policy actions beyond disclosure such as increasing funding for independent research and strict guidelines to regulate the interaction of research institutes with commercial entities. Public Health Implications. The influence on the research agenda has given the industry the potential to affect policymaking by influencing the type of evidence that is available and the kinds of public health solutions considered. The results of our scoping review support the need to develop strategies to counteract corporate influence on the research agenda.
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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 | 0.150 | 0.421 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.046 | 0.039 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".