Codeveloping a Virtual Patient Simulation to Foster Nurses’ Relational Skills Consistent With Motivational Interviewing: A Situation of Antiretroviral Therapy Nonadherence
Bibliographic record
Abstract
BACKGROUND: Although helping people living with HIV manage their antiretroviral therapy is a core competency of HIV nursing care, no educational intervention has sought to strengthen this competency. Thus, we codeveloped a simulation of a virtual patient (VP) having difficulty adhering to treatment to foster the relational skills that nurses require in such situations. OBJECTIVE: This viewpoint paper aims to describe the codevelopment process and the content of VP simulation, as well as the challenges encountered and the strategies used to overcome them. METHODS: We use a collaborative and iterative approach to develop the simulation based on qualitative evidence, theoretical approaches (strengths-based nursing, motivational interviewing [MI], and adult learning theories), and expert recommendations. We carried out 2 main phases: (1) planning the simulation development and (2) designing the simulation content, sequence, and format. We created the script as if we were writing a choose-your-own-adventure book. All relational skills (behavior change counseling techniques derived from MI) were integrated into a nurse-patient dialogue. The logic of the simulation is as follows: if the nurse uses techniques consistent with MI (eg, open-ended questions, summarizing), a dialogue is opened up with the VP. If the nurse uses relational skills inconsistent with MI (eg, providing advice without asking for permission), the VP will react accordingly (eg, defensively). Learners have opportunities to assess and reflect on their interventions with the help of quizzes and feedback loops. RESULTS: Two main challenges are discussed. The most salient challenge was related to the second phase of the VP simulation development. The first was to start the project with divergent conceptions of how to approach the VP simulation-the simulation company's perspective of a procedural-type approach versus the clinical team's vision of a narrative approach. As a broad strategy, we came to a mutual understanding to develop a narrative-type VP simulation. It meshed with our conception of a nurse-patient relationship, the values of strengths-based nursing (a collaborative nurse-patient relationship), and the MI's counseling style. The second challenge was the complexity in designing realistic relational skills in preprogrammed and simulated nurse-patient dialogue while preserving an immersive learning experience. As a broad strategy, we created a collaborative and work-in-progress writing template as a shared working tool. CONCLUSIONS: Our experience may be helpful to anyone looking for practical cues and guidance in developing narrative VP simulations. As relational skills are used by all nurses-from novices to experts-and other health care practitioners, focusing on this clinical behavior is a good way to ensure the simulation's adaptability, sustainability, and efficiency.
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.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 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".