Community engagement and the human infrastructure of global health research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Biomedical research is increasingly globalized with ever more research conducted in low and middle-income countries. This trend raises a host of ethical concerns and critiques. While community engagement (CE) has been proposed as an ethically important practice for global biomedical research, there is no agreement about what these practices contribute to the ethics of research, or when they are needed. DISCUSSION: In this paper, we propose an ethical framework for CE. The framework is grounded in the insight that relationships between the researcher and the community extend beyond the normal bounds of the researcher-research participant encounter and are the foundation of meaningful engagement. These relationships create an essential "human infrastructure" - a web of relationships between researchers and the stakeholder community-i.e., the diverse stakeholders who have interests in the conduct and/or outcomes of the research. Through these relationships, researchers are able to address three core ethical responsibilities: (1) identifying and managing non-obvious risks and benefits; (2) expanding respect beyond the individual to the stakeholder community; and (3) building legitimacy for the research project. SUMMARY: By recognizing the social and political context of biomedical research, CE offers a promising solution to many seemingly intractable challenges in global health research; however there are increasing concerns about what makes engagement meaningful. We have responded to those concerns by presenting an ethical framework for CE. This framework reflects our belief that the value of CE is realized through relationships between researchers and stakeholders, thereby advancing three distinct ethical goals. Clarity about the aims of researcher-stakeholder relationships helps to make engagement programs more meaningful, and contributes to greater clarity about when CE should be recommended or required.
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.
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 | 0.061 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| 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 it