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.
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 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 | 0.023 | 0.078 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".