Core Competences of School Nurses for the Development of Anti-Bullying Strategies: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: School nurses are capable of fostering safe and healthy school environments that are favorable to quality learning and social interactions. To this end, it is essential that they acquire a set of skills needed to implement bullying intervention programs. This article describes the protocol for a scoping review to identify and map the core competences for school nurses to develop anti-bullying strategies. METHODS: The review will be conducted according to the JBI methodology for scoping reviews. The review will include primary, secondary, and gray literature, including theses and reports, found through comprehensive research in several databases: Scopus, WoS, APA PsycINFO, Embase, ScienceDirect, MEDLINE/PubMed, CINAHL (EBSCOhost), ERIC, LILACS, BDENF, IBECS, Cochrane Library, CAPES Dissertations and Theses Portal, RCAAP, Theses Canada, ProQuest Dissertations and Theses, and Google Scholar, as well as reference tracking. No geographical restrictions will be applied. The studies must include investigations into actions and interventions conducted by or involving nurses for the prevention of bullying in the school context. Two reviewers will act independently in screening the studies and extracting data using an extraction tool developed by the research team. RESULTS: The results will be presented in a tabular format, supported by a narrative synthesis. The details of the scoping review will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. CONCLUSIONS: We anticipate that our scoping review will to strengthen a field of nursing that is still little explored, showing the school nurses' role in prevent bullying episodes.
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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 | 0.125 | 0.114 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.017 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.064 | 0.014 |
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".