The implementation of non‐weight focused approaches in clinical practice: A Canadian cross‐sectional study among registered dietitians
Bibliographic record
Abstract
BACKGROUND: An increasing number of dietitians use non-diet approaches, referred to as non-weight focused practice approaches (NWFAs), in clinical practice when working with higher weight adult clients. However, the factors that impact dietitians' ability to successfully implement these approaches in practice are unknown. METHODS: Aiming to examine how implementing NWFAs in clinical practice differs based on the extent to which a dietitian uses NWFAs with their clients, we conducted a cross-sectional online survey among Canadian registered dietitians who work with higher weight adults (May to July 2021), developed and validated following the Consolidated Framework for Implementation Research. Descriptive statistics were conducted to identify barriers and facilitators with respect to implementing NWFAs. The Kruskal-Wallis was used to test for differences in barriers and facilitators with respect to implementing NWFAs among five different practice approaches. The results showed that, among participants (n = 383; 82% white; 95% women) the most important barriers for implementation of NWFAs were clients' focus on weight as an outcome, when losing weight is a condition to access enhanced services, requiring changes to their practice philosophy, difficulty funding professional development and not having sufficient skills or knowledge to implement NWFAs in practice. Top-rated facilitators included the use of clinical guidelines, scientific publications and educational materials, which were rated with higher agreement across all implementation stages (p < 0.001). CONCLUSIONS: The present study highlights important factors that may impact the effective implementation of NWFAs in dietetic practice for higher weight adult clients, which is essential to minimise barriers in 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".