Nursing Activities for Patients With Chronic Disease in Primary Care Settings
Bibliographic record
Abstract
BACKGROUND: Nurses in primary care organizations play a central role for patients with chronic disease. Lack of clarity in role description may be associated with underutilization of nurse competencies that could benefit the growing population of patients with chronic disease. OBJECTIVE: The purpose of the research was to describe nursing activities in primary care settings with patients with chronic disease. METHODS: A Web-based survey was sent to nurses practicing in Family Medicine Groups in the Canadian Province of Québec. Participants rated the frequency with which they carried out nursing activities in five domains: (a) global assessment, (b) care and case management, (c) health promotion, (d) nurse-physician collaboration, and (e) planning services for patients with chronic disease. Findings were summarized with descriptive statistics (means, standard deviations, and ranges). RESULTS: The survey was completed by 266 of the 322 nurses who received the survey (82.6%). Activities in the health promotion and global assessment of the patient domains were carried out most frequently. Planning services for patients with chronic disease were least frequently performed. DISCUSSION: This study provides a broad description of nursing activities with patients with chronic disease in primary care. The findings provide a baseline for clinicians and researchers to document and improve nursing activities for optimal practice for patients with chronic disease.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".