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Record W1539652149 · doi:10.1080/1754730x.2015.1040040

Pilot testing a professional development model for preservice teachers in the area of health and weight: feasibility, utility, and efficacy

2015· article· en· W1539652149 on OpenAlexaff
Shelly Russell‐Mayhew, Sarah Nutter, Alana Ireland, Tina Gabriele, Angela D. Bardick, Jackie Crooks, Gavin Peat

Bibliographic record

VenueAdvances in School Mental Health Promotion · 2015
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDietingProfessional developmentPsychologyContext (archaeology)Faculty developmentTeacher educationSelf-efficacyMedical educationScale (ratio)Health professionalsPedagogyMedicineHealth careSocial psychologyWeight loss

Abstract

fetched live from OpenAlex

Studies indicate that both preservice and in-service teachers find it difficult to connect to their role as health promoters within a school context. There is also evidence that those teachers most often responsible for delivering health education (i.e., physical education teachers) are at an increased risk for body dissatisfaction, dieting, and disordered eating. A pre–post pilot study assessed the feasibility and utility of an interactive professional development workshop on preservice teachers' attitudes concerning body image, size acceptance, eating, and physical activity, as well as the impact of the workshop on perceived self-efficacy to address weight-related issues. The professional development had a positive effect on antifat attitudes, body image, implicit weight bias, and efficacy to address weight issues. While the workshop was useful in terms of significant changes in preservice teachers' attitudes and efficacy, lessons around feasibility will inform the development of this pilot study to full-scale workshop with preservice teachers.

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)0.018
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.334
GPT teacher head0.522
Teacher spread0.188 · 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

Citations17
Published2015
Admission routes1
Has abstractyes

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