Desafios à Gestão do Desempenho: análise lógica de uma Política de Avaliação na Vigilância em Saúde
Bibliographic record
Abstract
Acknowledging the contributions of the assessment area in supporting the performance of health policies, is to admit it in an ongoing and permanent way in the management context. This requires a set of procedures that go beyond monitoring and evaluation practices, known as performance management. The goal of this study was to analyze the logic of the Health Surveillance (HS) Evaluation Policy of Pernambuco, comparing it with the corresponding Canadian policy. For this purpose, a qualitative study of logical analysis of the program theory was carried out, using as a tool the design of the logical model of performance management and its respective matrix of analysis and judgment with the criteria to be evaluated. In HS, 9 key-informants were interviewed, and documents were analyzed; the Canadian model was analyzed based on a paper written by Lahey (2010). Both policies analyzed by this study are convergent and have the necessary elements for performance management. While the evaluation featured largely in the Canadian model, monitoring was the driving force behind the institutionalization of assessment practices in HS. Some lessons learned in the Canadian model can be recommended, such as the development of an assessment plan, based on the strategic and decision-making level of HS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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; both teacher heads agree on what is shown here.
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".