Virtual patient simulation to improve nurses’ relational skills in a continuing education context: a convergent mixed methods study
Bibliographic record
Abstract
BACKGROUND: Effective provider-patient communication is crucial to the delivery of high-quality care. Communication roadblock such as righting reflex is widely observed among providers and can lead to relational disengagement. In previous work, nurses felt ill-equipped to communicate effectively with HIV-positive patients to support medication adherence. Providing nurses with continuing education opportunities to improve their relational skills is a major target for optimizing the quality of care. Virtual patient simulation is one promising strategy that needs to be evaluated among graduate nurses. This study aimed to assess the acceptability of a virtual patient simulation to improve nurses' relational skills in a continuing education context. METHODS: We conducted a convergent mixed methods study by combining a quantitative pre-experimental, one-group post-test design and a qualitative exploratory study. We used convenience and snowball sampling approaches to select registered nurses (n = 49) working in Quebec, Canada. Participants completed an online sociodemographic questionnaire, consulted the automated virtual patient simulation (informed by motivational interviewing), and filled out an online post-test survey. Descriptive statistics (mean, SD, median, interquartile range) were used to present quantitative findings. From the 27 participants who completed the simulation and post-test survey, five participated in a focus group to explore their learning experience. The discussion transcript was subjected to thematic analysis. At the final stage of the study, we used a comparison strategy for the purpose of integrating the quantitative and qualitative results. RESULTS: Nurses perceived the simulation to be highly acceptable. They rated the global system quality and the technology acceptance with high scores. They reported having enjoyed the simulation and recommended other providers use it. Four qualitative themes were identified: motivations to engage in the simulation-based research; learning in a realistic, immersive, and non-judgmental environment; perceived utility of the simulation; and perceived difficulty in engaging in the simulation-based research. CONCLUSIONS: The simulation contributed to knowledge and skills development on motivational interviewing and enhanced nurses' self-confidence in applying relational skills. Simulation holds the potential to change practice, as nurses become more self-reflective and aware of the impact of their relational skills on patient care. TRIAL REGISTRATION: ISRCTN18243005 , retrospectively registered on July 3 2020.
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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 | 0.035 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".