Effect of implementing the 5<scp>A</scp>s of <scp>O</scp>besity <scp>M</scp>anagement framework on provider–patient interactions in primary care
Bibliographic record
Abstract
WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT: Obesity counselling in primary care is positively associated with self-reported behaviour change in patients with obesity. Obesity counselling is rare, and when it does occur, it is often of low quality because of poor training and/or competency of providers' obesity management, lack of time and economical disincentives, and negative attitude towards obesity and obesity management. 5As frameworks are routinely used for behaviour-change counselling and addiction management (e.g. smoking cessation), but few studies have examined its efficacy for weight management. WHAT THIS STUDY ADDS: This study presents pilot data from the implementation and evaluation of an obesity management tool (5As of Obesity Management developed by the Canadian Obesity Network) in a primary care setting. Results show that the tool facilitates weight management in primary care by promoting physician-patient communications, medical assessments for obesity and plans for follow-up care. Obesity remains poorly managed in primary care. The 5As of Obesity Management is a theory-driven, evidence-based minimal intervention designed to facilitate obesity counselling and management by primary care practitioners. This project tested the impact of implementing this tool in primary care clinics. Electronic self-administered surveys were completed by pre-screened obese subjects at the end of their appointments in four primary care clinics (over 25 healthcare providers [HCPs]). These measurements were performed before (baseline, n = 51) and 1 month after implementing the 5As of Obesity Management (post-intervention, n = 51). Intervention consisted of one online training session (90 min) and distribution of the 5As toolkit to HCPs of participating clinics. Subjects completing the survey before and after the intervention were comparable in terms of age, sex, body mass index, comorbidities, satisfaction and self-reported health status (P > 0.2). Implementing the 5As of Obesity Management resulted in a twofold increase in the initiation of obesity management (19 vs. 39%, P = 0.03), and caused a statistically significant increase in the perceived follow-up/coordination efforts (self-reported Patient Assessment of Chronic Illness Care components, 45 ± 22 vs. 67 ± 12 points, P = 0.002), as well as two components of the 5As framework: Assess (50 ± 29 vs. 66 ± 15 points, P = 0.03) and Assist (54 ± 26 vs. 72 ± 13 points, P = 0.01). Our results suggest that using the 5As of Obesity Management facilitates weight management in primary care by promoting physician-patient communications, medical assessments for obesity and plans for follow-up 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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".