Undergraduate, Female, Nutrition Students’ Perceptions of Curricular Influence on Attitudes toward Individuals with Obesity
Bibliographic record
Abstract
PURPOSE: This qualitative study, guided by a phenomenological approach, explored senior-level undergraduate, nutrition students' perceptions of how obesity and weight bias were addressed in the undergraduate curricula and how the curricula influenced their attitudes toward individuals with obesity. METHODS: Twenty senior-level undergraduate, nutrition students from the University of Guelph participated in interviews. Interviews were audio-recorded and transcribed verbatim. Thematic analysis entailed open, axial, and selective coding. RESULTS: Participants' sources of information about obesity in the curricula included nutrition courses, case studies, and non-nutrition courses. Regarding sources of information about weight bias in the curricula, they discussed nutrition courses, non-nutrition courses, and limited coverage of weight bias. Themes for curricular influence on attitudes toward people with obesity were increased knowledge of obesity, understanding the complexity of obesity, increased empathy toward individuals with obesity, and better ability to avoid stereotypes toward people with obesity. CONCLUSIONS: The perceptions among nutrition students varied regarding the amount and type of obesity and weight-bias information in the curricula, as well as the influence of the curricula on attitudes toward individuals with obesity, suggesting that obesity and weight bias warrant more coordinated coverage in the nutrition curricula.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".