Adapting ethical guidelines for adolescent health research to street-connected children and youth in low- and middle-income countries: a case study from western Kenya
Bibliographic record
Abstract
BACKGROUND: Street-connected children and youth (SCCY) in low- and middle-income countries (LMIC) have multiple vulnerabilities in relation to participation in research. These require additional considerations that are responsive to their needs and the social, cultural, and economic context, while upholding core ethical principles of respect for persons, beneficence, and justice. The objective of this paper is to describe processes and outcomes of adapting ethical guidelines for SCCY's specific vulnerabilities in LMIC. METHODS: As part of three interrelated research projects in western Kenya, we created procedures to address SCCY's vulnerabilities related to research participation within the local context. These consisted of identifying ethical considerations and solutions in relation to community engagement, equitable recruitment, informed consent, vulnerability to coercion, and responsibility to report. RESULTS: Substantial community engagement provided input on SCCY's participation in research, recruitment, and consent processes. We designed an assent process to support SCCY to make an informed decision regarding their participation in the research that respected their autonomy and their right to dissent, while safeguarding them in situations where their capacity to make an informed decision was diminished. To address issues related to coercion and access to care, we worked to reduce the unequal power dynamic through street outreach, and provided access to care regardless of research participation. CONCLUSIONS: Although a vulnerable population, the specific vulnerabilities of SCCY can to some extent be managed using innovative procedures. Engaging SCCY in ethical research is a matter of justice and will assist in reducing inequities and advancing their health and human dignity.
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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 | 0.053 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.015 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".