Canadianizing and Evaluating a Virtual Simulation Program for Community Health
Bibliographic record
Abstract
Nurse educators are looking to integrate innovative pedagogies to enable students to acquire required competencies in community/population health nursing. Previously, nursing students who used Sentinel City®, an American-based virtual simulation program for community health clinical learning have been shown to obtain equal or better learning outcomes compared to students who used traditional methods. To improve the fidelity of this virtual simulation program for our Canadian context, we Canadianized Sentinel City® to improve Canadian students’ learning experiences further. The purpose of this research was to describe the development of Sentinel City® Canada and subsequent evaluation of student learning outcomes after implementation across different sites in two provinces in Canada. Guided by constructivist and experiential learning concepts, we used a mixed-methods, cross-sectional survey. The quantitative questions were analyzed using descriptive statistics. Inferential (ANOVA) statistics examined the relationship between the use of Sentinel City® Canada and ability to meet their course learning outcomes. Qualitative data was analyzed using thematic analysis following a six-step process: 1) become familiar with the data; 2) generate initial codes; 3) search for themes; 4); review themes; 5) define themes; and 6) write up the findings. The study population included all currently registered nursing students (n = 396) in post-secondary nursing programs at two universities and one college during the 2021–2022 academic year who completed their community/public/population health nursing clinical with the use of Sentinel City® Canada. The response rate was 18% (n = 72). Learning outcomes of students who used Sentinel City® Canada varied across jurisdictions. The overall mean of students indicating that Sentinel City® Canada helped them achieve course objectives has increased from our previous studies. In fact, the overall mean of students indicating that Sentinel City® Canada helped them achieve course objectives has increased from our previous studies, with a mean of 2.47 in 2020, to a mean of 3.11 in 2022, to a mean of 3.34 with the Canadian version. Qualitative responses provide further insight into students’ perceptions. Canadianizing Sentinel City® has increased the fidelity of this community/population health simulation program and contributed to increasing student learning outcomes. Our findings provide evidence that Sentinel City® Canada can be a valuable learning tool for community/population health nursing clinical education that contributes to course learning outcomes.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".