A Randomized Waitlist-Controlled Trial of a Virtual Reality Intervention for Enhancing Professional Commitment, Resilience, and Coping in Long-Term Care Workers
Bibliographic record
Abstract
Background: Long-term care (LTC) workers face complex challenges requiring enhanced professional commitment and resilience. Virtual reality (VR) teaching modules provide immersive training, yet their effects on this workforce remain underexplored. This study examined the effectiveness of a VR-based module in enhancing professional commitment, psychological resilience, and coping strategies among LTC workers. Methods: A randomized waitlist-controlled trial was conducted with 92 LTC workers, divided equally into experimental and control groups. The experimental group received VR-based training, while the control group received the same intervention 4 weeks later. Data were analyzed using generalized estimating equations (GEE) and t -tests. Results: The VR module significantly improved professional commitment ( B = 7.24, p = .021). No statistically significant changes were observed for resilience or coping strategies. Conclusions and Application to Practice: VR-based training modules appear to enhance professional commitment among LTC workers. Integrating VR modules into mandatory training may help enhance job satisfaction, reduce burnout, and potentially improve care outcomes. Tailored VR training with workshops and peer engagement offers a practical approach to strengthening occupational health in LTC.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".