Developing a Performance Measurement Framework and Indicators for Community Health Service Facilities in Urban China
Bibliographic record
Abstract
BACKGROUND: China has had no effective and systematic information system to provide guidance for strengthening PHC (Primary Health Care) or account to citizens on progress. We report on the development of the China results-based Logic Model for Community Health Facilities and Stations (CHS) and a set of relevant PHC indicators intended to measure CHS priorities. METHODS: We adapted the PHC Results Based Logic Model developed in Canada and current work conducted in the community health system in China to create the China CHS Logic Model framework. We used a staged approach by first constructing the framework and indicators and then validating their content through an interactive process involving policy analysis, critical review of relevant literature and multiple stakeholder consultation. RESULTS: The China CHS Logic Model includes inputs, activities, outputs and outcomes with a total of 287 detailed performance indicators. In these indicators, 31 indicators measure inputs, 64 measure activities, 105 measure outputs, and 87 measure immediate (n = 65), intermediate (n = 15), or final (n = 7) outcomes. CONCLUSION: A Logic Model framework can be useful in planning, implementation, analysis and evaluation of PHC at a system and service level. The development and content validation of the China CHS Logic Model and subsequent indicators provides a means for stronger accountability and a clearer sense of overall direction and purpose needed to renew and strengthen the PHC system in China. Moreover, this work will be useful in moving towards developing a PHC information system and performance measurement across districts in urban China, and guiding the pursuit of quality in PHC.
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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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".