Building a strong public health nursing workforce in Canada: A continuing education solution
Bibliographic record
Abstract
Building a strong public health nursing (PHN) work-force capable of advancing population health and reducing inequities is critical. Though undergraduate nursing education is expected to provide introductory knowledge and practice of PHN in Canada, this is not always sufficient to adequately prepare nursing graduates for the complexity of PHN practice. To be practice ready for the full scope of PHN roles and interventions, new baccalaureate nurses and new registered nurses in public health are required to apply PHN competencies, theory, and knowledge of nursing and public health sciences, and to practice within the mandates of provincial and territorial public health legislation. To advance practice readiness a formal continuing education program is essential to foster these critical roles in PHN. This article describes the development of a postgraduate continuing education program for preparation to practice in PHN.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".