Seeking consent to genetic and genomic research in a rural Ghanaian setting: A qualitative study of the MalariaGEN experience
Bibliographic record
Abstract
BACKGROUND: Seeking consent for genetic and genomic research can be challenging, particularly in populations with low literacy levels, and in emergency situations. All of these factors were relevant to the MalariaGEN study of genetic factors influencing immune responses to malaria in northern rural Ghana. This study sought to identify issues arising in practice during the enrollment of paediatric cases with severe malaria and matched healthy controls into the MalariaGEN study. METHODS: The study used a rapid assessment incorporating multiple qualitative methods including in depth interviews, focus group discussions and observations of consent processes. Differences between verbal information provided during community engagement processes, and consent processes during the enrollment of cases and controls were identified, as well as the factors influencing the tailoring of such information. RESULTS: MalariaGEN participants and field staff seeking consent were generally satisfied with their understanding of the project and were familiar with aspects of the study relating to malaria. Some genetic aspects of the study were also well understood. Participants and staff seeking consent were less aware of the methodologies employed during genomic research and their implications, such as the breadth of data generated and the potential for future secondary research.Moreover, trust in and previous experience with the Navrongo Health Research Centre which was conducting the research influenced beliefs about the benefits of participating in the MalariaGEN study and subsequent decision-making about research participation. CONCLUSIONS: It is important to recognise that some aspects of complex genomic research may be of less interest to and less well understood by research participants and that such gaps in understanding may not be entirely addressed by best practice in the design and conduct of consent processes. In such circumstances consideration needs to be given to additional protections for participants that may need to be implemented in such research, and how best to provide such protections.Capacity building for research ethics committees with limited familiarity with genetic and genomic research, and appropriate engagement with communities to elicit opinions of the ethical issues arising and acceptability of downstream uses of genome wide association data are likely to be important.
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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 | 0.028 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".