Public health in undergraduate nursing education and workforce readiness
Bibliographic record
Abstract
Student engagement with the community is a cornerstone of undergraduate nursing education in Canada. Working with community from perspectives of social justice, health equity, advocacy, and political action are essential for workforce readiness. We suggest that the erosion of public health theory and clinical courses in baccalaureate nursing programs undermines the potential capability of nurses to address the intersectionality of the social determinants of health. The impact of the COVID-19 pandemic on populations further demonstrates inequities, particularly among marginalized populations. Public health nursing education supports students' understanding about the health impacts of social injustice, how systemic racism is embedded in colonial and Eurocentric structures, and practices of superiority and privileges.We, as a national group of public health nursing educators, set out to investigate how existing guidelines and competencies support public health in undergraduate education across Canada. Results from a national questionnaire of educators, and of PHN leaders on new graduate practice readiness are presented. Questionnaire responses confirm an erosion of PHN theory and practice in baccalaureate nursing education (BNE) curricula. The results of the questionnaires combined with evidence of PHN since the global pandemic provide educators and practitioners more insight to inform future directions to respond to workforce readiness.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".