Current Weight Management Approaches Used by Primary Care Providers in Six Multidisciplinary Healthcare Settings in Ontario
Bibliographic record
Abstract
Background Obesity management in primary care has been suboptimal due to lack of access to allied health professionals, time, and resources. Purpose To understand the weight management approaches used by primary care providers working in team-based settings and how they assess the most suitable approach for a patient. Methods A total of 20 primary care providers (13 nurse practitioners and 7 family physicians) working in 6 multidisciplinary clinics in Ontario were interviewed. All interviews were recorded, transcribed verbatim, and coded using NVivo qualitative software. Conventional content analysis was used to inductively elucidate codes, which were then clustered into categories. Results A referral to on-site programming was the most frequent weight management approach used. The pharmacological approach was underutilized due to adverse side effects and cost to patients. Primary care providers assessed the most suitable weight management approach based on patients': preference, level of motivation, income status and access to resources, body mass index and comorbidities, and previous weight loss attempts. Primary care providers perceived that referring to health professionals and educational resources were the approaches preferred by patients. Conclusions The team-based nature of these clinics allowed for referrals to various on-site professionals and/or programs. Some barriers to pursuing weight management avenues with patients were patient dependent.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".