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Record W2395019288 · doi:10.1080/10640260490267724

Prevention Series

2004· article· en· W2395019288 on OpenAlexaff
Niva Piran

Bibliographic record

VenueEating Disorders · 2004
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyCurriculumConstructiveExperiential learningEating disordersConsciousnessPedagogyConsciousness raisingMedical educationMathematics educationMedicineClinical psychology

Abstract

fetched live from OpenAlex

In the growing literature on the prevention of eating disorders, especially the research literature, the role of teachers in prevention is often missing. However, surveys with teachers reveal that there is an interest in eating disorder prevention by school staff and that teachers are seeking training that allows for more constructive interactions between teachers and students in areas related to body image. In addition, these surveys shown that teachers are interested in integrating preventative material into the curriculum. This article suggests that enlisting teachers towards the goal of primary prevention of eating disorders involves three key components: informing and raising consciousness about central issues related to the experience of the body, focus groups and experiential exercises that invite teachers to examine and constructively utilize the impact of their own past body-anchored experiences on current behavior as teachers, and collaborative brain storming about possible teachers'initiatives in integrating this new knowledge into their own classroom and into their schools. This article suggests that this work will allow teachers to provide students with constructive daily experiences in their classes in the area of body image.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.582
Threshold uncertainty score0.596

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.000
Scholarly communication0.0030.002
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.5820.343

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 head0.016
GPT teacher head0.312
Teacher spread0.297 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations45
Published2004
Admission routes1
Has abstractyes

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