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Record W2157821690 · doi:10.1002/pits.21853

BULLYING PREVENTION: A CALL FOR COLLABORATIVE EFFORTS BETWEEN SCHOOL NURSES AND SCHOOL PSYCHOLOGISTS

2015· article· en· W2157821690 on OpenAlexaboutno aff
Joan Kub, Marissa A. Feldman

Bibliographic record

VenuePsychology in the Schools · 2015
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsnot available
Fundersnot available
KeywordsHealth promotionPublic healthPsychologyCharterCall to actionMedical educationIntervention (counseling)NursingPublic relationsPedagogyMedicinePolitical science

Abstract

fetched live from OpenAlex

Bullying among children and adolescents is recognized as a significant global public health problem, as it has serious health consequences. Schools are important sites in which to address violence prevention, specifically bullying prevention, and to promote positive youth development. The Ottawa Charter for Health Promotion outlines five action points (building healthy public policy, creating supportive environments, strengthening community actions, developing personal skills, and reorienting health services) that should be considered when creating a positive school climate focused on health promotion. Using the position statements of school nurses and school psychologists, we outline their complementary roles in bullying prevention and intervention. In addition, because the global and national school health models call for building partnerships and enhancing communication among professionals within the schools and with the community to achieve health goals, we discuss how these two professional groups can work collaboratively to address bullying as a school health issue.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.066
metaresearch head score (Gemma)0.069
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.066
Threshold uncertainty score0.350

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0660.069
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.001
Science and technology studies0.0140.008
Scholarly communication0.0140.014
Open science0.0070.029
Research integrity0.0190.034
Insufficient payload (model declined to judge)0.0120.004

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.

Opus teacher head0.216
GPT teacher head0.555
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations83
Published2015
Admission routes1
Has abstractyes

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