Predictors of Weight Bias in Exercise Science Students and Fitness Professionals: A Scoping Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Although previous studies have reported weight bias among students and professionals in exercise science, physical education, kinesiology, and fitness instruction, predictors of weight bias in these professions have not been extensively reviewed. Aim: The purpose of this scoping review was to explore the available literature on predictors of weight bias in exercise science students and fitness professionals to identify key concepts and research gaps. Methods: . Six main themes were drawn from these studies including beliefs in the personal controllability of weight; sex differences; enrollment in a health sciences-related program; psychosocial and personal factors; knowledge of obesity; lack of personal history, family, or friend with obesity. Our scoping review highlighted diverse predictors of weight bias among exercise science students and professionals that warrant further study and intervention.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it