Whose shoulders is health research standing on? Determining the key actors and contents of the prevailing biomedical research agenda
Bibliographic record
Abstract
BACKGROUND: Conflicts of interest in biomedical research can influence research results and drive research agendas away from public health priorities. Previous agenda-setting studies share two shortfalls: they only account for direct connections between academic institutions and firms, as well as potential bias based on researchers' personal beliefs. This paper's goal is to determine the key actors and contents of the prevailing health and biomedical sciences (HBMS) research agenda, overcoming these shortfalls. METHODS: We performed a bibliometric and lexical analysis of 95,415 scientific articles published between 1999 and 2018 in the highest impact factor journals within HBMS, using the Web of Science database and the CorText platform. HBMS's prevailing knowledge network of institutions was proxied with network maps where nodes represent affiliations and edges the most frequent co-authorships. The content of the prevailing HBMS research agenda was depicted through network maps of prevalent multi-terms found in titles, keywords, and abstracts. RESULTS: The HBMS research agendas of large private firms and leading academic institutions are intertwined. The prevailing HBMS agenda is mostly based on molecular biology (40% of the most frequent multi-terms), with an inclination towards cancer and cardiovascular research (15 and 8% of the most frequent multi-terms, respectively). Studies on pathogens and biological vectors related to recent epidemics are marginal (1% of the most frequent multi-terms). Content of the prevailing HBMS research agenda prioritizes research on pharmacological intervention over research on socio-environmental factors influencing disease onset or progression and overlooks, among others, the study of infectious diseases. CONCLUSIONS: Pharmaceutical corporations contribute to set HBMS's prevailing research agenda, which is mainly focused on a few diseases and research topics. A more balanced research agenda, together with epistemological approaches that consider socio-environmental factors associated with disease spreading, could contribute to being better prepared to prevent and treat more diverse pathologies and to improve overall health outcomes.
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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 | 0.094 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.031 | 0.042 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.024 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".