Social Accountability and Accreditation: Impacting Health System Performance and Population Health
Bibliographic record
Abstract
Today’s reality all over the world has shown a huge disparity in the quality, equity, relevance, partnership and efficiency in the provision of health services resulting in huge gap of health status in many societies across the globe, be it in the developed and the developing countries. A number of reasons have been discussed. One most important reason is the disconnected between medical schools and health profession education institutions with their ecosystem and community they are mandated to serve. The concept of social accountability endorsed by the WHO since 1995 has not really been embraced by medical and health profession education institutions and not yet supported by key policy makers and health managers in many regions and countries. A few case studies have proved that the concept of social accountability is feasible and managable; and it eventually brings beneficial impact for the society in improving the health status. Existing guidelines and approaches could be used to accelerate the adoption of social accountability as long as key actors from international, national, institution and community levels are orchestrated congruently. A new paradigm in school’s accreditation embracing social accountability concept could reinforce this venture.
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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 | 0.037 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".