Development of a public health nurse professional practice model using participatory action research
Bibliographic record
Abstract
Public health nurses (PHNs) are ideally situated to reduce health inequities and based on documents articulating their role, should be working upstream to promote equity, prevent chronic diseases, and improve population health outcomes. In reality however, numerous barriers contribute to lack of role clarity for PHNs, and this goal has not been attainable in practice. A common vision for PHN practice based on discipline specific competencies and full scope of practice has been identified as a priority by Canadian experts. The intention of this study was to develop a model to support PHN practice in an urban Canadian city. This study used a participatory action research approach, grounded in local experience and context. The action was the development of a professional practice model. Data were gathered using semi-structured interview guides during audio-recorded research working group (RWG) meetings from November 2012 to July 2013. A researcher reflexive journal and field notes were kept. The data were analyzed using qualitative methods. A significant feature was full participant involvement throughout the course of the study. A professional practice model was a key organizational tool that provided the framework to develop an autonomous PHN role and the structures necessary to support PHN practice within the health system. The professional practice model fostered full scope of practice and role clarity, with a focus on population health and equity, so that a consistent and evidence-based practice was attainable. The result was that RWG participants reported a shift in their practice, with greater awareness of theory. Participatory action research was essential in developing the framework and common language, and is a research methodology that should continue to be explored with nurses in Canada.
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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 | 0.049 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.004 | 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".