“Interest‐holders”: A new term to replace “stakeholders” in the context of health research and policy
Bibliographic record
Abstract
Background: Given the colonial connotations of the term "stakeholder", its continued use may be perceived as disrespectful to Indigenous Peoples. While several groups have introduced alternative terms, each has its own limitations. The objective of this article is to introduce "interest-holders" as an alternative term to "stakeholders" and describe the discussions underpinning the adoption of the new term by the MuSE Consortium. Methods: The MuSE Consortium is an international network of over 160 individuals with interest and expertise in different aspects relevant to engagement in research. Members of MuSE explored alternative terms and considered their respective merits and limitations. The deliberations considered the literature on the topic and the results of two consultations with the wider MuSE membership on the alternative terms. Results: We define "interest-holders" as groups with legitimate interests in the health issue under consideration. The interests arise and draw their legitimacy from the fact that people from these groups are responsible for or affected by health-related decisions that can be informed by research evidence. Conclusion: As groups other than the MuSE Consortium have started to adopt "interest-holders," we hope its use will reduce confusion related to the multitude of terms used and convey the intended meaning without any negative connotations.
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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 | 0.147 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.009 | 0.098 |
| Scholarly communication | 0.012 | 0.035 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".