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Record W3109671732 · doi:10.1177/0829573520974916

The Efficacy of Comprehensive School Health Course in Changing Pre-Service Teachers’ Attitudes and Reactions Toward Weight-Related Teasing

2020· article· en· W3109671732 on OpenAlexafffund
Jessica F. Saunders, Sarah Nutter, Isabel Brun, Deinera Exner‐Cortens, Shelly Russell‐Mayhew

Bibliographic record

VenueCanadian Journal of School Psychology · 2020
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of VictoriaUniversity of Calgary
FundersJ.W. McConnell Family FoundationPublic Health Agency of Canada
KeywordsVignettePsychologyPerceptionObligationMedical educationDevelopmental psychologySocial psychologyMedicine

Abstract

fetched live from OpenAlex

Teachers view overt expressions of weight stigma, such as weight-related teasing, as troublesome but are often ill-prepared to address instances of weight-related teasing when they arise in the classroom. Comprehensive school health is an emerging framework that aims to better prepare teachers to address issues of health and wellness in the classroom, including weight-related teasing. We examined the efficacy of a university course in comprehensive school health in changing pre-service teachers’ attitudes and perceptions of weight-related teasing. Pre-service teachers read a vignette and responded to 10 items about the vignette at both the beginning and end of the semester-long course. Items relating to pre-service teachers’ perception of, responsibility and obligation to address the weight-related teasing event showed significant, positive change from the beginning to end of the semester. These results suggest that exposure to developmentally sound methods of delivering health-related content can support adaptive educator reactions to weight-related teasing.

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)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.124
GPT teacher head0.466
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 designObservational
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

Citations3
Published2020
Admission routes2
Has abstractyes

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