Nursing Contributions to Chronic Disease Management in Primary Care
Bibliographic record
Abstract
BACKGROUND: As the prevalence of chronic diseases continues to increase, emphasis is being placed on the development of primary care strategies that enhance healthcare delivery. Innovations include interprofessional healthcare teams and chronic disease management strategies. OBJECTIVE: To determine the roles of nurses working in primary care settings in Ontario and the extent to which chronic disease management strategies have been implemented. METHODS: We conducted a cross-sectional survey of a random sample of primary care nurses, including registered practical nurses, registered nurses, and nurse practitioners, in Ontario between May and July 2011. RESULTS: Nurses in primary care reported engaging in chronic disease management activities but to different extents depending on their regulatory designation (licensure category). Chronic disease management strategy implementation was not uniform across primary care practices where the nurses worked. CONCLUSIONS: There is the potential to optimize and standardize the nursing role within primary care and improve the implementation of chronic disease management strategies.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".