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Record W2118177323

Preparing School Health Facilitators: Building Competence and Confidence for a New Role

2015· article· en· W2118177323 on OpenAlexaff
Kate Storey, Genevieve Montemurro, Margaret Schwartz, Anna Farmer, Paul J. Veugelers

Bibliographic record

VenueRevue phénEPS / PHEnex Journal · 2015
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCompetence (human resources)Medical educationHealth promotionPsychologyMedicineNursingPublic health
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Comprehensive school health (CSH) is an internationally recognized framework that provides opportunities for children and youth to develop health-enhancing behaviours while improving educational outcomes. However, there is little information on how to effectively train individuals to implement CSH. The purpose of this research was to describe the development and evaluation of a training program designed to prepare facilitators to work collaboratively with school communities to implement CSH. METHODS: Ten facilitators from a CSH program were purposefully sampled and invited to participate in a self-administered open-ended structured interview immediately after completion of the training program and again one year later. RESULTS: Analyses revealed that how the training was designed and implemented was equally as important as the content, and that building confidence was as important as building competence. CONCLUSIONS: The findings are relevant to those interested in preparing school health facilitators and health promotion practitioners for practice in the field.

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)0.012
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.121
GPT teacher head0.463
Teacher spread0.342 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations4
Published2015
Admission routes1
Has abstractyes

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Same venueRevue phénEPS / PHEnex JournalSame topicSchool Health and Nursing EducationFrench-language works237,207