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Record W2437045573

The Role of the CSH School Principal in Knowledge Sharing and Use

2016· article· en· W2437045573 on OpenAlexaffabout
Erica Roberts, Kerry Bastian, John Paul Ekwaru, Paul J. Veugelers, Doug Gleddie, Kate Storey

Bibliographic record

VenueRevue phénEPS / PHEnex Journal · 2016
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOddsPrincipal (computer security)Logistic regressionKnowledge sharingSet (abstract data type)Survey data collectionPsychologyMedical educationKnowledge managementComputer scienceMedicineMathematicsStatistics
DOInot available

Abstract

fetched live from OpenAlex

Comprehensive School Health (CSH) is an internationally recognized framework shown to be effective in improving health-enhancing behaviours and educational outcomes. The specific implementation strategies behind CSH, however, are vague. Knowledge exchange (KE) practices are essential to ensure that implementation is evidence-informed. The principal seemingly acts as a key player within KE, yet this role remains to be examined within a CSH framework. Through a cross-sectional examination of secondary survey data, this study set out to compare the extent of knowledge sharing and use of evaluation data by principals in both CSH schools (n=30) and other randomly selected schools throughout Alberta, Canada (n=73). Univariable logistic regression was performed and results showed that CSH principals had a statistically significant higher odds of: sharing the data overall; sharing the data outside of the school, particularly with parents; using the data in planning; as well as both sharing and using the data in general.

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)0.015
metaresearch head score (Gemma)0.033
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 score0.104
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.033
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.004
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.077
GPT teacher head0.394
Teacher spread0.317 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

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Same venueRevue phénEPS / PHEnex JournalSame topicCommunity Health and DevelopmentFrench-language works237,207