Bridging ethics and culture: a co-created, culturally sensitive informed consent framework for research in Bimoba and Mamprusi communities in Ghana
Bibliographic record
Abstract
Informed consent (IC) is a cornerstone of ethical research, yet standard models grounded in Western, individual-focused principles often require contextual adaptation in collectivist settings. This study co-created a culturally responsive IC framework for research with the Bimoba and Mamprusi ethnic groups in Ghana, operationalizing respect for autonomy in ways that reflect local values and decision-making norms. We employed a qualitative, cross-sectional design involving interviews and focus group discussions with community leaders, members, and stakeholders from academia, NGOs, and ethics committees. Thematic analysis identified culturally endorsed recruitment practices, resulting in a four-step framework: community entry, independent mediation at households, invitation of eligible participants, and a culturally embedded, multi-step consent process. The framework was pilot-tested in four communities and developed through a collaborative, multistakeholder process. While grounded in IC principles, the framework reflects broader community engagement values, reinforcing relational autonomy, trust, and cultural legitimacy. Rather than rejecting standard models, it offers a context-sensitive adaptation that enables ethical and legitimate recruitment. This study informs research ethics in the Global South and offers practical guidance for researchers, ethics committees, and institutions in similar contexts.
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 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 | 0.485 | 0.257 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.012 | 0.043 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.007 | 0.013 |
| 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; the direct Gemma label and the distilled Codex classifier 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".