Intuitive eating and Health at Every Size in community settings: Dietitian's perceptions of practice barriers
Bibliographic record
Abstract
Intuitive Eating (IE) and Health at Every Size (HAES) are health promotion paradigms used by dietitians in private practice more commonly than in community-based practice where more diverse and vulnerable populations are served. The primary objective of this study was to examine the perceived barriers and facilitators that dietitians encounter when using IE and HAES in community nutrition practice settings. This phenomenological, qualitative study applied a grounded theory analysis to identify emergent themes from transcripts of semi-structured interviews with 27 dietitians working in community settings in the United States. Dietitians reported the following perceived barriers to the use of IE/HAES: diet culture which was often expressed as inconsistent messages patients receive from the media and other professionals that conflict with nutrition providers’ messages; legislative restrictions and weight-centric administrative policies; and personal beliefs of clients and colleagues concerning weight and health. Dietitians reported occupational autonomy as a salient factor facilitating the use of IE/HAES in community practice and identified the need for shifts in attitudes about weight and its relation to health achieved through research and dissemination of information on weight-inclusive practices. Collectively, respondents experienced more systemic barriers than individual barriers and identified several macro-level facilitators that remain elusive. The unique experiences of RDNs in community practice provide a roadmap for ongoing research to establish the evidence base for best practices, inform education and training, and achieve cultural shifts that move towards weight-inclusive practice in this setting. More research is needed to explore the generalizability of these experiences.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".