Estratégias de gerenciamento na Atenção Primária à Saúde em territórios de vulnerabilidade social expostos à violência
Bibliographic record
Abstract
OBJECTIVE: To identify management strategies used by the Family Health Strategy teams of a Basic Health Unit in organizing work in socially vulnerable territories exposed to violence. METHOD: A single case study with a qualitative approach in a family health unit located in the southern region of Brazil. Data collection was conducted through individual interviews with 27 health professionals from August to September 2017 and a focus group with 18 participants in April 2018. Data organization and processing was performed with the support of the IRAMUTEQ software program and subsequently the content analysis technique. RESULTS: The five classes characterized strategies used by professionals to provide care to the population considering their experience in facing violent situations. A guideline was developed and validated in the focus group to guide the management and organization of work in these services. CONCLUSION: It was evidenced that professionals develop strategies which include strengthening the team as a form of collective protection, welcoming focused on comprehensive care and bonding, even without the support of specific public policies for these situations. The population is allied to facilitate access to care for vulnerable people and alerts professionals to critical situations in the territory.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| 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".