Cardiopulmonary Resuscitation Training by Avatars: A Qualitative Study of Medical Students’ Experiences Using a Multiplayer Virtual World
Bibliographic record
Abstract
BACKGROUND: Emergency medical practices are often team efforts. Training for various tasks and collaborations may be carried out in virtual environments. Although promising results exist from studies of serious games, little is known about the subjective reactions of learners when using multiplayer virtual world (MVW) training in medicine. OBJECTIVE: The objective of this study was to reach a better understanding of the learners' reactions and experiences when using an MVW for team training of cardiopulmonary resuscitation (CPR). METHODS: Twelve Swedish medical students participated in semistructured focus group discussions after CPR training in an MVW with partially preset options. The students' perceptions and feelings related to use of this educational tool were investigated. Using qualitative methodology, discussions were analyzed by a phenomenological data-driven approach. Quality measures included negotiations, back-and-forth reading, triangulation, and validation with the informants. RESULTS: Four categories characterizing the students' experiences could be defined: (1) Focused Mental Training, (2) Interface Diverting Focus From Training, (3) Benefits of Practicing in a Group, and (4) Easy Loss of Focus When Passive. We interpreted the results, compared them to findings of others, and propose advantages and risks of using virtual worlds for learning. CONCLUSIONS: Beneficial aspects of learning CPR in a virtual world were confirmed. To achieve high participant engagement and create good conditions for training, well-established procedures should be practiced. Furthermore, students should be kept in an active mode and frequent feedback should be utilized. It cannot be completely ruled out that the use of virtual training may contribute to erroneous self-beliefs that can affect later clinical performance.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".