Bioethics Recommendations to Increase Culturally Informed Global Health Survey Research: A Framework for Centering Community Engagement
Bibliographic record
Abstract
Global health projects-a source of inspiration and collaboration for applied human biology-benefit scholars, governments, NGOs, and aid organizations. While such research is intended to improve population health, direct benefits to individuals and communities are often excluded from published works and/or not considered in study designs and framing. This exclusion is increasingly recognized as a colonial legacy that hinders global health equity, particularly for Indigenous and other marginalized populations. Collaboration and community engagement are avenues for addressing these injustices, but they require planning, intention, and resources. Drawing on our collective experience and ongoing dialogues about community engagement in human biology, we propose six recommendations to increase equity in global health research. These include: (1) Incorporating trusted local specialists and stakeholders at all project levels; (2) disseminating health information to participants in strengths-based and culturally meaningful ways and contributing to solutions wherever possible; (3) investing in local healthcare, research, and infrastructure; (4) making study results/data available to stakeholders; (5) working within data frameworks that respect community sovereignty; and, (6) applying culturally informed bioethics frameworks. Our discussion highlights persistent needs to address community rights and benefits and to dismantle colonial legacies within global health and human biology while recognizing structural barriers to implementing these needed changes, particularly within the context of global health projects wherein human biologists are not the main power brokers or resource holders. When interfacing with global health, human biologists must continue to pursue health equity and decolonization through implementing critical, culturally informed bioethics frameworks centering community engagement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.035 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.006 |
| 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, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".