The intersections of industry with the health research enterprise
Bibliographic record
Abstract
There is increased awareness of the negative impact of large multinational corporations - the 'industry' - on public health. These corporations have established different types of relationships with a number of actors in the field of health research. This Commentary explores the different types of relationships between the industry and the actors of health research, how they intersect with the different research steps, and how these relationships allow the industry to exert influence. The types of relationships discussed consist of funding of research, direct relationships with the actors of research (namely advocacy groups, funding agencies, experts, professional organisations, regulatory agencies and health practitioners), and the influencing research standards. The potentially influenced research steps either precede the research (i.e. the prioritisation of research question), relate to it directly (i.e. its planning, conduct, reporting, dissemination and evaluation), or build on it (i.e. regulatory approval, integration into guidelines and adoption into practice). In conclusion, the industry has successfully fostered relationships with almost every actor of the health research enterprise and is using these relationships to influence the different steps of health research. The degree of influence the industry is having on health research calls for more work on managing the relationships discussed herein.
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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 | Metaresearch Domain: Incentives · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Incentives · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | 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 | 0.045 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.011 |
| 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.
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".