Advanced Access in Primary Health Care: experiences from Brazil and Canada
Bibliographic record
Abstract
Access is the timely use of services in order to meet the needs of the user. As an alternative to the traditional model, the Advanced Access (AA) model appears in Canada , with schedules restricted to specific cases. Objectives: The purpose of this article is to analyze the existing scientific production about the Advanced Access model in Primary Health Care. Methods: Integrative literature review using the descriptors (1) “Health Care Accessibility” OR “Primary Health Care” AND and (2) "Advanced Access" in the PubMed, Scopus and BIREME databases. Selection criteria were studies published in the last five years, available in Portuguese, English or Spanish and dealing with the theme. Results: Eight studies were selected, six were grouped into two categories of analysis: “The Canadian experience with AA : a model in consolidation”; and “The Brazilian experience with AA: local experiments” and the other two contributed to enrich the discussion. The AA stands out to balance capacity and demand with physical infrastructure and adequate staff, both in the international arena, as the experiences of municipalities, improving the quality of APS. Conclusion: Studies that detail the AA in its practice, as well as the challenges and needs, can inspire other health units to study it and consider its implementation if it is appropriate for its context, aiming to improve the health and care of its population.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.013 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".