<i>Canadian Dietitians’ Understanding of</i> Non-Dieting Approaches in Weight Management
Bibliographic record
Abstract
PURPOSE: How Canadian dietitians define and use non-dieting and size acceptance approaches (SAAs) in the context of weight management was explored. METHODS: Fifteen focus groups with 104 dietitians were conducted in seven Canadian cities. Questions were designed to explore participants' understanding and use of non-dieting and SAAs, including counselling goals, techniques, and outcome measures. Sessions were tape-recorded, transcribed verbatim, coded, and analyzed using qualitative methods. RESULTS: Participants generally agreed that non-dieting involves promoting healthy lifestyles and avoiding restrictive diets. Participants also agreed that size acceptance means accepting all body shapes and sizes and promoting comfort with one's body. Many dietitians said they use size acceptance only with appropriate clients, most often with those who are lighter or without other health risks. Others said that size acceptance, by definition, is appropriate for everyone. Opinions varied about the appropriateness of teaching portion sizes or using meal plans, and whether weight loss could be a goal of non-dieting and SAAs. CONCLUSIONS: Views on the usefulness of non-dieting and size acceptance strategies in weight management counselling were related, at least partially, to the different understanding that dietitians had of these approaches. Terminology needs to be clarified when we speak about non-dieting and SAAs. The varied understanding about these concepts should help dietitians reflect on their own perspectives and practice.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".