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Record W4205800678 · doi:10.1186/s12912-021-00740-x

Virtual patient simulation to improve nurses’ relational skills in a continuing education context: a convergent mixed methods study

2022· article· en· W4205800678 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueBMC Nursing · 2022
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversité du Québec à RimouskiUniversité LavalUniversité de MontréalThe Quebec Population Health Research NetworkCentre Hospitalier de l’Université de MontréalCentre de Santé et de Services Sociaux Cavendish
FundersCanadian Institutes of Health ResearchMinistère de l'Éducation et de l'Enseignement supérieurMax-Planck-GesellschaftRéseau de recherche portant sur les interventions en sciences infirmières du Québec
KeywordsSnowball samplingSimulated patientThematic analysisContext (archaeology)MedicineDescriptive statisticsNursingInterviewMedical educationTest (biology)Qualitative propertyFocus groupQualitative researchPsychologyComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.424
Teacher spread0.396 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it