Advanced Nursing Practices in the Care of People with Diabetes in Primary Health Care
Bibliographic record
Abstract
Abstract Objective To analyze nursing care for people with diabetes mellitus from the perspective of Advanced Nursing Practices in Primary Health Care. Methods Cross-sectional and analytical study involving primary care nurses from four municipalities in a state in southern Brazil. An online instrument was used, based on national guidelines for diabetes care and the Modified Scale for Advanced Nursing Practices (EMDF/ANP), Brazilian version, using a score from 0 (I do not perform) to 4 (I always perform). The data were analyzed using the Statistical Package for the Social Sciences (SPSS). Results A total of 121 responses from primary care nurses were analyzed, with questions on 53 direct actions in the care of people with diabetes. Considering the average score, 4 items received a score between 0.0 and <1.0 (not performed), 6 items between 1.0 and <2.0 (incipient action), 19 items between 2.0 and <3.0 (action being developed), 23 items from 3.0 to 4.0 (established action). The items related to direct and comprehensive care had an average score of 2.78 (median 3). Nurses indicated the need for further training, especially in continuing education, and 91% agreed with the statement that Advanced Nursing Practices contribute to improving care for people with chronic conditions. Conclusion Nurses perform activities within the scope of Advanced Nursing Practices for people with diabetes, in their clinical aspects. It is essential to ensure their expanded scope of practice, organizational, social, and training strategies, also considering aspects of leadership, education, and research.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".