Interprofessional collaboration in diabetes care: perceptions of family physicians practicing in or not in a primary health care team
Bibliographic record
Abstract
BACKGROUND: In Canada, most patients with type 2 diabetes mellitus (T2DM) are cared for in the primary care setting in the practices of family physicians. This care is delivered through a variety of practice models ranging from a single practitioner to interprofessional team models of care. This study examined the extent to which family physicians collaborate with other health professionals in the care of patients with T2DM, comparing those who are part of an interprofessional health care team called a Primary Care Network (PCN) to those who are not part of a PCN. METHODS: Family physicians in Alberta, Canada were surveyed to ascertain: which health professionals they refer to or have collaborative arrangements with when caring for T2DM patients; satisfaction and confidence with other professionals' involvement in diabetes care; and perceived effects of having other professionals involved in diabetes care. Chi-squared and Fishers Exact tests were used to test for differences between PCN and non-PCN physicians. RESULTS: 170 (34%) family physicians responded to the survey, of whom 127 were PCN physicians and 41 were non-PCN physicians (2 not recorded). A significantly greater proportion of PCN physicians vs non-PCN physicians referred patients to pharmacists (23.6% vs 2.6%) or had collaborative working arrangements with diabetes educators (55.3% vs 18.4%), dietitians (54.5% vs 21.1%), or pharmacists (43.1% vs 21.1%), respectively. Regardless of PCN status, family physicians expressed greater satisfaction and confidence in specialists than in other family physicians or health professionals in medication management of patients with T2DM. Physicians who were affiliated with a PCN perceived that interprofessional collaboration enabled them to delegate diabetes education and monitoring and/or adjustment of medications to other health professionals and resulted in improved patient care. CONCLUSIONS: This study sheds new insight on the influence that being part of a primary care team has on physicians' practice. Specifically, supporting physicians' access to other health professionals in the primary care setting is perceived to facilitate interprofessional collaboration in the care of patients with T2DM and improve patient 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 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) | 0.003 | 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".