Nutrition and obesity care in multidisciplinary primary care settings in Ontario, Canada: Short duration of visits and complex health problems perceived as barriers
Bibliographic record
Abstract
Nutrition care in the primary care setting is integral in obesity management, but there is a substantial gap between patients who would benefit from this service and those receiving it. This study provides an in-depth understanding of how relatively new multidisciplinary primary care settings may be affecting nutrition care practices of family physicians and nurse practitioners. This qualitative comparative case study sought to assess nutrition care practices in three different types of multidisciplinary clinics (2 Family Health Teams, 3 Community Health Centers, 1 Nurse Practitioner-Led Clinic) in Ontario, Canada. Individual semi-structured interviews were conducted with nurse practitioners (n = 13) and family physicians (n = 7) in fall 2017. Data analysis was conducted using NVivo Software. The integrated approach was used for elucidating codes and themes. Findings suggest that suboptimal duration of medical visits and increasing prevalence of complex patients were reported by most participants and were perceived as barriers for addressing nutrition and obesity. However, improved nutrition care was fostered through Electronic Medical Records, primary care providers' positive attitude towards nutrition and cost-free dietitian services at point of access. Site-specific challenges, such as duration of medical visits, incentive programs, access to dietitians on site, and continued professional development could enhance nutritional care for weight management in these multidisciplinary primary care settings.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.003 |
| 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".