Rights-Based Citizen Monitoring in Peru: Evidence of Impact from the Field.
Bibliographic record
Abstract
This paper discusses a human rights-based initiative developed in Puno, Peru, in which indigenous women seek to address problems with access and quality of care by monitoring their government-run health facilities. The evidence of impact presented here is based on a qualitative study of the rights-based monitoring initiative (53 key informant interviews in 2010-2011), corroborated by findings from a review of previous qualitative and quantitative assessments of the initiative. The research findings show that the citizen monitors are able to identify, document, and act on a set of persistent "everyday injustices" experienced by health care users. These can include illegal financial charges, abusive or dismissive treatment, extended wait times, and culturally insensitive care. These results suggest that citizen monitoring can lead to important changes at the health facility level, as well as in the lives of the volunteer monitors. It can also provide key information that can be used to put previously neglected concerns onto local and national health policy agendas. However, as this article explores, the citizen monitoring initiative faces several of its own challenges.
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".