Improving implementation of best practices in obesity management: Physician experiences in obesity care
Bibliographic record
Abstract
In this study, we sought to analyse experiences in weight management among physicians working in the area of obesity and contrast these experiences with best practices. By understanding experiences of physicians working in obesity management, we can better support implementation of best practices in their day-to-day practice. An online survey of Canadian primary care physicians, internists and endocrinologists recruited from a nationwide market research database was conducted. The survey captured demographic characteristics and perceptions about weight loss and its management. One hundred and ninety-two physicians (140 primary care, 22 internists and 30 endocrinologists) were recruited and completed the survey. Challenges identified by the physicians in helping patients lose weight included patients' poor compliance and lack of time and resources to address the issue. Most physicians reported considering obesity to be a chronic disease, but most did not incorporate a multi-dimensional, chronic disease model of obesity treatment (i.e., combination of lifestyle interventions with psychological, medical and/or surgical interventions). Endocrinologists reported management practices consistent with a chronic disease model more frequently than primary care physicians. These data highlight the need for improvement in obesity management, particularly in primary care. Despite proliferation of guidelines on best practices, implementation of these practices into daily practice remains low.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".