Equity analysis of relative value quantification based performance appraisal in primary health care
Bibliographic record
Abstract
Objective To analyze the equity of the performance appraisal model of primary health care institutions based on the relative value index. Methods Tian Tan Community Health Service Center in Dong Cheng District of Beijing was taken as an example, while descriptive analysis and correlation analysis were conducted using the data of service equivalent per capita and monthly performance income per capita of 14 departments from April 2018 to June 2019. Results In the first half of 2019, the average monthly work equivalent of the case institution was 1 170.19±501.18, which was 13.91% higher than that of the second quarter in 2018. The average monthly performance income of the case institution was 1 183.71±175.30 Yuan, which was 6.94% higher than that of the second quarter in 2018.From April 2018 to June 2019, the monthly work equivalent per capita of 14 departments was positively correlated with the monthly performance income per capita, and Pearson correlation coefficient was 0.85(P<0.01). Conclusions The performance appraisal model based on the relative value index in case institution satisfactorily reflects the fairness of more work and more gain , but the effect of better work and better gain is not obvious. Key words: Community health services; Performance appraisal; Relative value index; Quantification; Equity
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".