Evaluation at the Time of Health Systems Reform: Chinese Policymakers’ Need for a Robust System of Evaluations to Assess Progress in the Implementation of Reform Efforts
Bibliographic record
Abstract
Abstract In 2014 the authors of this paper, evaluators from China and Canada, jointly interviewed 12 policymakers in China at the national and provincial levels. This paper describes the needs of policymakers and how they view the evaluation capacity‐building needs of the health system. The learnings from the policymaker dialogues informed the evaluation capacity‐building efforts at three pilot sites in China. This paper focuses on the evaluation capacities needed by policymakers and implementers specifically to address inequities in the health sector, unlike most publications that focus on evaluation capacities of researchers.
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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 | 0.400 | 0.312 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".