Leadership, administration and management in health care: a scoping review protocol of nurse education
Bibliographic record
Abstract
Abstract Introduction: Strategic leadership in nursing, represents one of the global goals at the current time. Although integrated into university programs, the teaching of administration and management, did not seem to promote the expected results. This finding may be a consequence of the teaching methods, competencies targeted, or students' perceptions of the usefulness and interest of the subject. A new theoretical framework is needed, with the identification of underpinning skills, key contents and teaching methodologies adapted to the current reality. Objective: Map the models and conceptions of teaching-learning nursing administration and management. Methods: This review will follow the JBI methodology, and will include all types of studies, published in English, French, Spanish, and Portuguese, on the last decade, without any geographical limitations. The databases included MEDLINE, CINAHL, Psychology and Behavioral Sciences Collection by EBSCO, Scopus (Elsevier), Canadian Science Publishing, grey literature (Google Scholar) and Repositório Científico de Acesso Aberto de Portugal (RCAAP). Data will be analysed and extracted by two independent researchers and will be presented in a specific table. Results: Previous bibliographic research allowed the identification of some concepts, theories and pedagogical strategies implemented in the teaching-learning process of the subject. Conclusion: The results will contribute to help instructors to improve their educational approach, adapting the theory of the subject through the use and development of teaching-learning strategies that can support and empower upcoming nurses, face to future challenges in healthcare.
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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 | 0.147 | 0.139 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.011 |
| Bibliometrics | 0.027 | 0.023 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.049 | 0.012 |
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".