Reorienting Public Health Nurses’ Practice With a Professional Practice Model
Bibliographic record
Abstract
Purpose Documents articulating public health nurses' (PHNs') roles, including Canadian standards and competencies, depict a broad focus working at multiple levels to improve population outcomes through the promotion of health equity. Conversely, Canadian experts depict a looming crisis, based on the rising disconnect between daily activities and ideal practice. While perfectly positioned, PHNs' skills and abilities are under-utilized and largely invisible. The intention of this study was to develop a model to support the full scope of equity-focused PHN practice. Method A participatory action research approach was used. Qualitative data were gathered using semistructured interview guides during audio-recorded meetings. The data were coded into central themes using content analysis and constant comparison. A researcher reflexive journal and field notes were kept. A significant feature was full participant involvement. Results The outcome was a professional practice model to reframe the PHN role to focus on population health and equity. The model was imperative in promoting full scope of practice, dealing with workload pressures, and describing PHNs' value within the organization and broader health system. Conclusion Professional practice models hold promise as frameworks to depict autonomous practice activities, situated within organizations and healthcare systems, and underpinned by nursing knowledge.
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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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".