Dietitians' perspectives on patient barriers and enablers to weight management: An application of the social‐ecological model
Bibliographic record
Abstract
AIM: Dietitians are nutrition experts who conduct nutrition assessments and provide support to patients seeking to manage their weight. The aim of the present study was to assess dietitians' perspectives on the barriers and enablers encountered by patients during their weight management journey. METHODS: Fourteen individual semi-structured interviews were conducted over a 3-month period in 2017 with dietitians working in Ontario, Canada. All interviews were audio-recorded and transcribed verbatim. Two researchers coded the data independently using a directed content analysis approach. RESULTS: Emerging themes were classified at societal, community, relationship, individual levels of the social-ecological model (SEM). Barriers included low socioeconomic status, 'go big or go home approach', lack of knowledge and cooking skills, lack of time, emotional eating, unsupportive home and work environments, discrimination and weight bias, lack of communication between health professionals, and low accessibility to healthy foods. Enablers included community programs, workplaces promoting healthy behaviours, and ongoing clinical support. Dietitians mentioned that patients encounter many barriers that may coexist and hinder weight management and/or maintenance of lost weight. CONCLUSIONS: Communication between health team members and ongoing patient support in the clinical setting are required. A whole-of-government, whole-of-society approach is needed to target the various aforementioned barriers at various level of the SEM.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".