A rapid assessment methodology for the evaluation of primary care organization and performance in Brazil
Bibliographic record
Abstract
INTRODUCTION: This study presents a methodology for the rapid assessment of the organization and performance of primary care services at district level. It compares results from an earlier provider survey in Petrópolis, Brazil with those obtained directly from users of two types of primary care services (newly developed Family Health Program services or 'PSF'--Programa de Saúde da Família--and traditional services) in the same municipality. The aim is to demonstrate the validity of the methodology and its potential use in measuring and improving district-level primary care services in developing countries. METHODS: The study adapted a previously developed questionnaire to measure essential dimensions of primary care. Users (n = 468) were randomly selected from each of the 40 primary care clinics in the district and administered a question survey. Responses were used to create nine measures: an overall composite 'total primary care index' and eight sub-indices each pertaining to an essential primary care dimension. RESULTS: Primary care services show considerable variation in user experiences. Users of the new (PSF) clinics reported higher overall assessments of the total primary care index and the sub-indices for gatekeeping, comprehensiveness, family focus and community orientation than did users of traditional services (P < 0.05). The total primary care score was internally consistent with a Cronbach's alpha of 0.8, and could be reduced to only one principal component. User assessments of primary care services were predicted by self-rated health (OR 1.72) and the site of care (OR 1.03). User and provider assessments of the total primary care index were not significantly different, disagreeing on only two sub-indices (gatekeeping and family focus, P < 0.05). CONCLUSIONS: The study presents a rapid and valid method of obtaining information about clinic-level variation in primary care organization and performance at district level. The total primary care index was not sensitive to demographic or socio-economic characteristics of clients, was internally consistent and appears valid given similar results obtained using two different data sources. With some adaptation the instrument could potentially be applied in other developing countries.
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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 | 0.034 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".