Nursing Practicums in Health Promoting Schools: A Quality-Improvement Project
Bibliographic record
Abstract
Educating new nurses on primary prevention and population health promotion can be challenging with the erosion of public health in Canada over the past decade. The lack of public health nursing positions can create difficulties when endeavoring to engage nursing students in understanding nursing’s role in population health promotion. To understand the challenges and improve efforts to educate new nurses with regard to community health, we conducted a quality-improvement project with nursing students in health-promoting schools from a service-learning perspective. We conducted qualitative interviews with 24 fourth-year nursing students and three health-promoting school principals and used thematic analysis within a service-learning framework. Several themes emerged from the interviews, including experiencing something new, creating meaning through reflection, achieving reciprocity, and providing direct benefits to stakeholders. Health-promoting schools both benefit and provide essential opportunities for nursing students. Undeniably, nursing students provide a much-needed resource to these schools, establishing valuable personal connections with pupils in a system that is underfunded – while witnessing its needs increase. Schools provide a venue to exercise the skills of nursing such as caring and understanding the importance of community health. Applying a service-learning approach can help nursing students to appreciate the opportunity and to maximize their learning in this context. The active role of community and public health nurses in school settings can provide a necessary connection to primordial and primary prevention, and in this context, nursing students can contribute to communities while gaining valuable insight into what it means to be a registered nurse.
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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 | 0.051 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".