Challenges in interdisciplinary weight management in primary care: lessons learned from the 5<scp>A</scp>s <scp>T</scp>eam study
Bibliographic record
Abstract
Increasingly, research is directed at advancing methods to address obesity management in primary care. In this paper we describe the role of interdisciplinary collaboration, or lack thereof, in patient weight management within 12 teams in a large primary care network in Alberta, Canada. Qualitative data for the present analysis were derived from the 5As Team (5AsT) trial, a mixed-method randomized control trial of a 6-month participatory, team-based educational intervention aimed at improving the quality and quantity of obesity management encounters in primary care practice. Participants (n = 29) included in this analysis are healthcare providers supporting chronic disease management in 12 family practice clinics randomized to the intervention arm of the 5AsT trial including mental healthcare workers (n = 7), registered dietitians (n = 7), registered nurses or nurse practitioners (n = 15). Participants were part of a 6-month intervention consisting of 12 biweekly learning sessions aimed at increasing provider knowledge and confidence in addressing patient weight management. Qualitative methods included interviews, structured field notes and logs. Four common themes of importance in the ability of healthcare providers to address weight with patients within an interdisciplinary care team emerged, (i) Availability; (ii) Referrals; (iii) Role perception and (iv) Messaging. However, we find that what was key to our participants was not that these issues be uniformly agreed upon by all team members, but rather that communication and clinic relationships support their continued negotiation. Our study shows that firm clinic relationships and deliberate communication strategies are the foundation of interdisciplinary care in weight management. Furthermore, there is a clear need for shared messaging concerning obesity and its treatment between members of interdisciplinary teams.
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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 | 0.040 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".