Consent to Research in Madagascar: Challenges, Strategies, and Priorities for Future Research
Bibliographic record
Abstract
The ethical conduct of research in any setting hinges on the voluntary and informed consent of research participants. Working towards consent that is truly voluntary and informed, however, is far from straightforward, and requires attention to contextual factors that may complicate achievement of this ideal in specific research settings. This paper is based on Madagascar’s first “Consent complexities in health research in Madagascar” workshop, held in Antananarivo, Madagascar, in October 2018. It identifies a number of challenges encountered by individuals responsible for the conduct or oversight of health research in Madagascar related to informed and voluntary consent. Key challenges identified included: adaptation of consent tools into local dialects and for limited literacy populations; perceived acquiescence of potential participants regardless of actual preference based on cultural norms; perceived time pressures within tight project timelines to collect data as quickly as possible, limited time for consent processes; fears and taboos related to specific research procedures or topics; and, uncertainty about how best to approach and verify the validity of individual consent in contexts where traditional leaders’ influence is conventionally sought out and respected. Potential strategies for responding to each of these challenges are proposed, as are key questions meriting further study.
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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 | 0.282 | 0.296 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.018 | 0.035 |
| Scholarly communication | 0.031 | 0.038 |
| Open science | 0.009 | 0.026 |
| Research integrity | 0.018 | 0.027 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".