Simulating a situation of homelessness: nursing students' perceptions of learning through virtual embodiment
Bibliographic record
Abstract
Individuals experiencing homelessness encounter unique challenges in accessing and receiving care in our health systems[1,2,3,4] Preparing emerging health professionals to respond to their complex health needs will require innovative educational approaches that promote person-centered care, and stimulate critical reflection and action towards the personal, interpersonal and structural factors that shape health care delivery.[5,6,7] This presentation reports on preliminary findings of phase 1 of a critical qualitative case study of nursing student’s perceptions of learning about the experience of homelessness, through a virtual reality educational experience. The study design was informed by critical transformative learning theories and theories of embodiment. Twenty nursing students were engaged in a virtual reality experience of 12 minutes, followed by a 1:1 debrief interview. The debrief interview used an adapted version of the Promoting Excellence and Reflective Learning in Simulation (PEARLS) framework to elicit students’ reflections on the experience. The interviews were audio recorded and transcribed verbatim. Data analysis involved a process of reading all of the transcripts for a sense of the whole, mindmapping each of the transcripts, identifying themes that permeated the data set, and coding data in Quirkos software. Six preliminary themes include: a) seeing the person through story, b) destabilizing assumptions and questioning stereotypes, c) embodied emotional awareness, d) challenges to care, e) recognizing vulnerability of people experiencing homelessness, and f) quality of the immersive experience in learning. The findings contribute to our knowledge about virtual reality simulation as an innovative approach to fostering learning about homelessness in health professions education.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".