Resolutividade no Subsistema de Atenção à Saúde Indígena (SASI-SUS): análise em um serviço de referência no Amazonas, Brasil
Bibliographic record
Abstract
Problem-solving is one of the principles of the Unified Health System (SUS) in Brazil, with its ability to solve the health problems of the population at different levels of complexity. The Indigenous Health Care Subsystem (SASI-SUS) is part of this service, respecting the specificities of indigenous populations. The scope of this article is to analyze the perception of professionals and managers of an Indigenous Health Center (CASAI) regarding its ability to cope with the circumstances of the pandemic. It involved qualitative and descriptive research under the National Health Care Policy for Indigenous Peoples (PNASPI) and Paul Ricoeur's hermeneutic theory. Interviews were conducted with participants in order to record the experiences in the work process of the actors who assist the indigenous people housed at CASAI. Four essential themes were identified in the interviews: cultural care; permanent education in health & health education; negotiation & improvisation; and reception & infrastructure. CASAI is an institution that is more than a support center or accommodation, being a crossover point between the different levels of care and knowledge production of the indigenous people, as well as a place for establishing a relationship, resulting in a problem-solving space.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".