An Examination of the Practice Approaches of Canadian Dietitians Who Counsel Higher-Weight Adults Using a Novel Framework: Emerging Data on Non-Weight-Focused Approaches
Bibliographic record
Abstract
Non-weight-focused approaches (NWFAs) may be used by some clinicians when working with higher-weight clients. In contrast to weight-focused approaches (WFAs), NWFAs de-emphasize or negate weight loss and emphasize overall diet quality and physical activity. The extent to which WFAs, NWFAs, or a combination of both WFAs and NWFAs are used by dietitians is unknown in Canada and globally. This study surveyed Canadian Registered Dietitians (RDs) who counsel higher-weight clients to assess which practice approaches are most commonly used, how they view the importance of weight, and how they define "obesity" for the study population. Five practice approaches were initially defined and used to inform the survey: solely weight-focused; moderately weight-focused; those who fluctuate between weight-focused/weight-inclusive approaches (e.g., used both approaches); weight inclusive and; weight liberated. Participants (n = 383; 94.8% women; 82.2% white) were recruited using social media and professional listservs. Overall, 45.4% of participants used NWFAs, 40.5% fluctuated between weight-focused/moderately weight-focused, and 14.1% used weight-focused approaches (solely weight focused and moderately weight focused). Many participants (63%) agreed that weight loss was not important for higher-weight clients. However, 81% of participants received no formal preparation in NWFAs during their education or training. More research is needed to understand NWFAs and to inform dietetic education in support of efforts to eliminate weight stigma and provide inclusive access to care.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 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".