Holograms in nursing education: Results of an exploratory study
Bibliographic record
Abstract
Objective: The purpose of this exploratory and descriptive study was to evaluate the student experience of using the Microsoft HoloLens® headsets and the HoloPatient application (app) to perform a nursing assessment of Jerry, a life-sized hologram of a young man admitted to Emergency Department following a mountain bike accident.Methods: Setting: The research was conducted in 2019 in a New Zealand School of Nursing. Participants were undergraduate (pre-licensure) students (N = 121) enrolled in a 3-year Bachelor of Nursing degree programme. The study was conducted before students went on their first hospital-based clinical placement. Methods: The researchers designed a tutorial that guided students through the first five steps of the clinical reasoning cycle (i.e., look, collect, process, decide, plan) and collect cues and information about Jerry’s condition which worsens as he develops anaphylactic shock. Tutorials were conducted during the week immediately preceding the first clinical placement to assist students to develop clinical reasoning and nursing assessment skills.Results: Data were collected via a post-activity pen and paper survey. Quantitative data showed that this technology enhanced learning. Thematic analysis identified 17 advantages of using holograms, including realism, a reduced level of self-consciousness, and better preparation for clinical practice. Disadvantages mostly related to technical projection issues such as blurry image quality.Conclusions: These findings indicate that spending time carefully observing, and processing information provided via a hologram assisted novice nurses to develop clinical reasoning skills, thereby increasing readiness for the clinical setting.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".