Commentary on “A Framework for Community and Stakeholder Engagement: Experiences From a Multicenter Study in Southern Africa”
Bibliographic record
Abstract
Community and stakeholder engagement (CSE) is increasingly acknowledged as foundational to global health research. This commentary builds on the multisite framework for CSE described in an eco-health study conducted in Southern Africa. We acknowledge the context-specific nature of some of the challenges for CSE and draw attention to significant issues and concerns that arose from our studies of CSE in the context of multisite HIV prevention trials in South Africa, India, and Canada: (a) Pretrial-historically based mistrust, identification of appropriate gatekeepers, and considering the breadth of community; (b) Trial implementation-impact of early trial cessations, appropriate community roles and responsibilities, and multifaceted stigma; and (c) Posttrial-supporting and sustaining CSE mechanisms independent of particular trials. Many of these challenges are exacerbated by widespread disparities in wealth and power between trial sponsors and participating communities, further supporting the central importance of sound CSE practices and infrastructures to advance ethical biomedical and public health research.
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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: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: yes | Qualitative | high |
| gpt | MetaresearchResearch integrity Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
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 | 0.055 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.021 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.044 | 0.051 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".