A Review of Eating Disorders and Disordered Eating amongst Nutrition Students and Dietetic Professionals
Bibliographic record
Abstract
The diet industry and media have a powerful influence over women, leading many to believe that they must modify their appearance for societal acceptance. Dietetics, as one of many predominantly female professions, may be particularly vulnerable to these pressures. An integrative review process was used to examine eating disorders and disordered eating within the dietetics profession with the aim to both synthesize existing data and develop questions for future research. Seventeen articles were reviewed using broad search terms and dates because of the dearth of available literature. Given nutrition programs and dietetic practice often involve significant exposure to food, ideas and opinions about food, weight, and its place in health and dietetic practice researchers were compelled to ask "why". Findings were organized under 3 categories including thinness ideology, implications of food and body associated with nutrition or dietetic education, and establishment of a continuum. This review serves as a platform to inspire future research in an understudied but important topic related to dietetic education and practice. Minimally as a profession, baseline data need to be collected to understand the prevalence of disordered eating and eating disorders along the continuum of practice in Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".