Identifying Strategies to Advance Health Equity through Action on Social Determinants of Health and Human Rights for Street-Connected Children and Youth in Kenya.
Bibliographic record
Abstract
Despite the fact that street-connected children and youth (SCY) in low- and middle-income countries experience numerous social and health inequities, few evidence-based policies and interventions have been implemented to improve their circumstances. Our study analyzed strategies to advance health equity through action on the social determinants of health (SDH) for SCY in Kenya based on General Comment 21 of the United Nations Committee on the Rights of the Child. To identify policies and interventions, we analyzed archival newspaper articles and policy documents and elicited ideas from a diversity of social actors across Kenya. Our results identified three types of policies and interventions: repressive, welfare oriented, and child rights based. We then situated these strategies within the World Health Organization's conceptual framework on SDH inequities to understand their mechanism of impact on health equity. Our results demonstrate that a child rights approach provides a strong avenue for advancing health equity through action on the SDH for SCY in Kenya. As a result of these findings, we developed a checklist for policy makers and other stakeholders to assess how their policies and interventions are upholding human rights, addressing needs, and working to advance health equity for SCY.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".