Access to justice: evaluating law, health and human rights programmes in Kenya
Bibliographic record
Abstract
INTRODUCTION: In Kenya, human rights violations have a marked impact on the health of people living with HIV. Integrating legal literacy and legal services into healthcare appears to be an effective strategy to empower vulnerable groups and address underlying determinants of health. METHODS: We carried out an evaluation to collect evidence about the impact of legal empowerment programmes on health and human rights. The evaluation focused on Open Society Foundation-supported legal integration activities at four sites: the Academic Model of Providing Access to Healthcare (AMPATH) facility, where the Legal Aid Centre of Eldoret (LACE) operates, in Eldoret; Kenyatta National Hospital's Gender-based Violence Recovery Centre, which hosts the COVAW legal integration program; and Christian Health Association of Kenya (CHAK) facilities in Mombasa and Naivasha. In consultation with the organizations implementing the programs, we designed a conceptual logic model grounded in human rights principles, identified relevant indicators and then coded structure, process and outcome indicators for the rights-related principles they reflect. The evaluation included a resource assessment questionnaire, a review of program records and routine data, and semi-structured interviews and focus group discussions with clients and service providers. Data were collected in May-August 2010 and April-June 2011. RESULTS: Clients showed a notable increase in practical knowledge and awareness about how to access legal aid and claim their rights, as well as an enhanced ability to communicate with healthcare providers and to improve their access to healthcare and justice. In turn, providers became more adept at identifying human rights violations and other legal difficulties, which enabled them to give clients basic information about their rights, refer them to legal aid and assist them in accessing needed support. Methodological challenges in evaluating such activities point to the need to strengthen rights-oriented evaluation methods. CONCLUSIONS: Legal empowerment programmes have the potential to promote accountability, reduce stigma and discrimination and contribute to altering unjust structures and systems. Given their apparent value as a health and human rights intervention, particularly for marginalized populations, further rigorous evaluations are called for to support the scale-up of such programmes.
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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 | 0.059 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".