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
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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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".