Finding the right balance: student perceptions of using virtual simulation as a community placement
Bibliographic record
Abstract
Abstract Objectives Finding appropriate community clinical placements has been challenging in recent years, most especially during the COVID-19 pandemic. During the 2020-2021 semesters, a university in the province of Alberta, Canada chose to use the community health virtual simulation program, Sentinel City®3.1 , to provide clinical placements for three groups of undergraduate students. This expository paper, co-authored by students and faculty, sought to further explore how virtual simulation can be used to best support student learning by identifying practices that students find most helpful. Method Jeffries’ (2005) simulation framework was used to guide a quality improvement analysis which explored feedback received from 16 students regarding the use of Sentinel City®3. 1 as a clinical placement, with additional contributions from the student co-authors. Results Students felt Sentinel City®3.1 was an effective tool to learn community and population health concepts, however, all students indicated that they would have preferred more opportunities to work with real communities. Conclusion Virtual simulation programs like Sentinel City®3.1 might be best as a learning supplement rather than as students’ sole clinical placement experience.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".