Team composition and chronic disease management within primary healthcare practices in eastern Ontario: an application of the Measuring Organizational Attributes of Primary Health Care Survey
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Various organizational-level attributes are being implemented in primary healthcare to improve healthcare delivery. There is a need to describe the distribution and nature of these attributes and explore differences across practices.AimThe aim of this study was to better understand organizational attributes of primary care teams, focusing specifically on team composition, nursing roles, and strategies that support chronic disease management. METHODS: We employed a cross-sectional survey design. Team composition, nursing roles, availability of health services, and chronic disease management activities were described using the 'Measuring Organizational Attributes of Primary Health Care Survey.'FindingsA total of 76% (n=26 out of 34) of practice locations completed the survey, including family health teams (FHT; n=21) and community health centers (CHC; n=4). Nurse practitioners (NPs) and registered nurses (RNs) were the most common non-physician providers, and CHCs had a greater proportion of non-physician providers than FHTs. There was overlap in roles performed by NPs and RNs, and registered practical nurses engaged in fewer roles compared with NPs and RNs. A greater proportion of FHTs had systematic chronic disease management services for hypertension, depression and Alzheimer's disease compared with CHC practices. The 'Measuring Organizational Attributes of Primary Health Care Survey' was a useful tool to highlight variability in organizational attributes across PHC practices. Nurses are prominent within PHC practices, engaging in a wide range of roles related to chronic disease management, suggesting a need to better understand their contributions to patient care to optimize their roles.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it