Evaluation of prenatal care in Primary Health Care in Brazil
Bibliographic record
Abstract
Abstract Objectives: to evaluate prenatal care in Primary Care by identifying the aspects that influence structural and operational adequacy. Methods: evaluation research with analysis of 4,059 municipalities that joined the 2nd cycle of the Program for Improving Access and Quality in Primary Care in 2013-2014. The evaluative model composed of 19 indicators grouped in structural aspects and operational aspects dimensions was validated in a consensus conference. Data analysis was descriptive, with the issuance of value judgment. Results: in structural aspects, 32.6% of the municipalities presented adequacy, whilst in operational ones, only 24.1%. In the general prenatal evaluation, less than a quarter (24.6%) of the municipalities was adequate, those with up to 10 thousand inhabitants had a higher percentage of adequacy (41.6%). The South region presented adequacy of 33.8%, considering all sizes. Conclusions: most municipalities presented low adequacy in prenatal care, with better performance of structural aspects. Smaller municipalities presented better results in all analyzed items. Structural aspects and general evaluation of prenatal care are highlighted in the South region. Adequate attention to prenatal care needs to be comprehensive and equitable, with the strengthening of regional networks geared towards social inclusion.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".