Application of Quality Measurement and Performance Standards to Public Health Systems
Bibliographic record
Abstract
In Brief To date, there have been few points of intersection between the quality work done in the general health system and performance review in the public health system. This article describes Washington State's set of performance standards for public health, the accreditation-type evaluation process, and some of the results of the recent performance evaluation against the Washington State Standards. Taking action on the evaluation results could enhance the capacity of public health to join general health systems in Washington State to address several of the priority areas described in Transforming Health Care Quality, the 2003 Institute of Medicine Report. This article describes Washington State's set of performance standards for public health, the accreditation-type evaluation process, and some of the results of the recent performance evaluation against the state standards.
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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 | 0.219 | 0.391 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".