Under the same roof: co-location of practitioners within primary care is associated with specialized chronic care management
Bibliographic record
Abstract
BACKGROUND: International and national bodies promote interdisciplinary care in the management of people with chronic conditions. We examine one facilitative factor in this team-based approach - the co-location of non-physician disciplines within the primary care practice. METHODS: We used survey data from 330 General Practices in Ontario, Canada and New Zealand, as a part of a multinational study using The Quality and Costs of Primary Care in Europe (QUALICOPC) surveys. Logistic and linear multivariable regression models were employed to examine the association between the number of disciplines working within the practice, and the capacity of the practice to offer specialized and preventive care for patients with chronic conditions. RESULTS: We found that as the number of non-physicians increased, so did the availability of special sessions/clinics for patients with diabetes (odds ratio 1.43, 1.25-1.65), hypertension (1.20, 1.03-1.39), and the elderly (1.22, 1.05-1.42). Co-location was also associated with the provision of disease management programs for chronic obstructive pulmonary disease, diabetes, and asthma; the equipment available in the centre; and the extent of nursing services. CONCLUSIONS: The care of people with chronic disease is the 'challenge of the century'. Co-location of practitioners may improve access to services and equipment that aid chronic disease management.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".