Bringing Theory to Life: Engaging Nursing Students in a Collaborative Population-Based Screening Project
Bibliographic record
Abstract
A collaborative population-based project for bowel cancer prevention provided an ideal opportunity to involve nursing students in applying theory to practice. In this article, described is how the engagement of students and subsequent application of a population health template contributed to a community-based bowel cancer education and screening campaign. The campaign was a valuable teaching-learning experience for students and contributed to the goal of reducing and reporting on the number of bowel cancer deaths in the local area. Project evaluation data provide insight into student learning outcomes and reveal ways to strengthen the population health initiative for future years. Originally, a scholarly pursuit of discovery and application developed in response to growing rates of bowel cancer and advances in effective screening programs, the project has evolved into the domain of teaching and learning. This evolution has benefited students, project organizers and community members.
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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 | 0.041 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.004 | 0.009 |
| 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".