BEST PRACTICES IN NURSING AND THEIR INTERFACE WITH THE EXPANDED FAMILY HEALTH AND BASIC HEALTHCARE CENTERS
Bibliographic record
Abstract
ABSTRACT Objective: to know and reflect on the best practices in nursing and their interface with the Expanded Family Health and Basic Healthcare Centers (NASF-AB). Method: this is a participatory research based on Paulo Freire’s methodological framework and developed from thematic investigation, coding, decoding, and critical unveiling. The information was produced and analyzed in four Culture Circles, with an average of five nurses and duration of two hours each, between April and June 2018. The investigation revealed four generating themes, unveiled during the meetings. In this study, the theme “best nursing practices that favor relations with NASF-AB” will be discussed. Results: nurses acknowledge communication as a tool that promotes best practices in nursing. It was possible to deepen the dialogue and knowledge about NASF-AB’s work process and the role of nursing. Nurses act as a link between the support team and the Family Health team, a skill resulting from their training focused on management, having leadership and dialogue as resources for conflict resolution. Conclusion: the present study contributed to improve nurses’ thinking and acting in relation to the proposed theme. The reflections made during Culture Circles boosted transformative attitudes in the practice settings. Nurse approximation with NASF-AB favors autonomy and collaborative practices (understood as best practices), encouraging interprofessional and solve-problem actions within Basic Care.
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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 | 0.031 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".