Avaliação da integralidade na atenção primária à saúde através da Primary Care Assessment Tool: revisão sistemática
Bibliographic record
Abstract
OBJECTIVE: To assess the comprehensiveness of primary healthcare (PHC) in different countries. METHOD: PubMed, Virtual Health Library (BVS), and Scopus were systematically searched. Observational studies published from 2017 to 2019, using the Adult Primary Care Assessment Tool (PCAT) to assess comprehensiveness were included without limits regarding language of publication or country. The quality of studies was assessed using the Newcastle-Ottawa scale (NOS). RESULTS: Of 124 articles initially selected, 13 were included: four from China, two from Japan, and two from Vietnam; considering the Americas, all four studies were performed in Brazil. Only one study from Africa, performed in Malawi, was included. The quality of studies according to the NOS was acceptable. Considering the availability of services, eight facilities had low comprehensiveness, vs. five with high comprehensiveness. Considering the services performed at the facility, nine had low comprehensiveness, and only four had high comprehensiveness. CONCLUSION: The low degree of PHC orientation in terms of comprehensiveness in terms of both services performed and services provided may reflect a lack of understanding of the demands of users, and indicates the need for concrete action to strengthen PHC as the basis of healthcare systems.
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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 | 0.063 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.049 | 0.049 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".