<i>Definitions of Healthy Eating</i> Among University Students
Bibliographic record
Abstract
PURPOSE: To identify definitions of healthy eating in terms of food characteristics, eating behaviours, barriers, and benefits in university students. METHODS: Four focus groups were conducted; verbatim transcripts were analyzed and coded using qualitative methods. Participants were nine students of dietetics and six students of other subjects. All were females in their third or fourth year at the University of British Columbia (UBC). RESULTS: Participants often described healthy eating as consuming all food groups of Canada's Food Guide to Healthy Eating, with the associated notions of moderation and balance. Benefits of healthy eating were cited as a healthy weight, good physical appearance, feeling better, preventing disease, and achieving personal satisfaction. Barriers to healthy eating included lack of time, choice, taste preferences, and finances. There was some discrepancy between what the dietetics students perceived as barriers for clients (e.g., lack of information), and barriers the potential clients (other students) perceived for themselves. CONCLUSIONS: As dietitians, we must try to understand our clients' definitions of healthy eating and their barriers to achieving it, which likely differ from our own.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".