Beyond procedural ethics: Foregrounding questions of justice in global health research ethics training for students
Bibliographic record
Abstract
Interest in global health is growing among students across many disciplines and fields of study. In response, an increasing number of academic programmes integrate and promote opportunities for international research, service or clinical placements. These activities raise a range of ethical issues and are associated with important training needs for those who participate. In this paper, we focus on research fieldwork conducted in lower income nations by students from more affluent countries and the ethics preparation they would benefit from receiving prior to embarking on these projects. Global health research is closely associated with questions of justice and equity that extend beyond concerns of procedural ethics. Research takes place in and is shaped by matrices of political, social and cultural contexts and concerns. These realities warrant analysis and discussion during research ethics training. Training activities present an opportunity to encourage students to link global health research to questions of global justice, account for issues of justice in planning their own research, and prepare for 'ethics-in-practice' issues when conducting research in contexts of widespread inequality. Sustained engagement with questions of justice and equity during research ethics training will help support students for involvement in global health research.
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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 | 0.088 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.098 |
| Scholarly communication | 0.028 | 0.021 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.012 | 0.026 |
| 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".