Assessing progress of the Pan American Health Organization's Policy Research for Health in member states
Bibliographic record
Abstract
The improvement of health in the twenty-first century is inextricably linked to research for health. In response to growing international appeal to address regional health needs, the Pan American Health Organization (PAHO) and its Member States approved the Policy on Research for Health (CD49/10) in 2009. This document represents the flagship regional policy on research for health and outlines how health systems and services in the region can be strengthened through research. It has been implemented by the two components of PAHO -the Member States and the Pan American Sanitary Bureau. The policy contained a specific directive mandating PAHO to report on its implementation, development of subsequent strategies, and action plans targeting its governing bodies. The Americas are the first World Health Organization (WHO) region to issue a regional Policy on Research for Health, which was harmonized with WHO's Strategy on Research for Health, approved in 2010. Attending to the recommendations issued by PAHO's Advisory Committee on Health Research and WHO's Advisory Committee on Health Research, the PAHO Department of Knowledge Management, Bioethics and Research set out to advance the assessment of the implementation of the Policy on Research for Health through the creation of a monitoring and evaluation Scorecard. Indicators relevant to the Policy on Research for Health objectives were mapped from the Compendium of Impact and Outcome Indicators, with new indicators created. A practical framework based on available indicator data was proposed to generate a baseline policy assessment and incorporate a means of incrementally enhancing the measurements. In this case study, we outline the iterations of the PAHO Policy on Research for Health Scorecard, as well as the lessons learned throughout the development process that may be a valuable guide for health research entities monitoring and evaluating the progress of their own policies.
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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 | 0.025 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".