A Virtual Simulator to Improve Weight-Related Communication Skills for Health Care Professionals: Mixed Methods Pre-Post Pilot Feasibility Study
Bibliographic record
Abstract
Background: Discussing weight remains a sensitive and often avoided topic in health care, despite rising prevalence of obesity and calls for earlier, more compassionate interventions. Many health care professionals report inadequate training and low confidence to discuss weight, while patients often describe feeling stigmatized or dismissed. Digital simulation offers a promising route to build communication skills through supporting repeatable and reflective practice in a safe space. VITAL-COMS (Virtual Training and Assessment for Communication Skills) is a novel simulation tool designed to support health care professionals in navigating weight-related conversations with greater understanding and skill. Objective: This study aimed to assess the potential of VITAL-COMS as a digital simulation training tool to improve weight-related communication skills among health care professionals. Methods: A mixed-method feasibility study was conducted online via Zoom (Zoom Video Communications) between January to July 2021, with UK-based nurses, doctors, and dietitians. The intervention comprised educational videos and 2 simulated patient scenarios with real-time verbal interaction. Pre- and posttraining self-assessments of communication skills and conversation length were collected. Participants also completed a feasibility questionnaire. Descriptive statistics were used to analyze the feasibility questionnaire, and open-ended feedback was analyzed using content analysis. Paired-samples t tests were used to assess changes in communication skills and conversation length before and post training. Results: In total, 31 participants completed the study. There was a statistically significant improvement in self-assessed communication skills following training (mean difference=3.9; 95% CI, 2.54-5.26; t30=-5.76, P=.001, Cohen d=1.03). Mean conversation length increased significantly in both scenarios: in the female patient scenario, from 3.73 (SD 1.36) to 6.08 (SD 2.26) minutes, with a mean difference of 2.35 minutes (95% CI, 1.71-2.99; t30=7.49, P=.001, Cohen d=1.34); and in the male scenario, from 3.61 (SD 1.12) to 5.65 (SD 1.76) minutes, a mean difference of 2.03 minutes (95% CI, 1.51-2.55; t30=8.03, P=.001, Cohen d=1.44). Participants rated the simulation positively, with 97% (95% CI 90%-100%) supporting wider use in health care and 84% (95% CI 71%-97%) reporting emotional engagement. Content analysis of feedback generated two themes: (1) adapting to this form of learning and (2) recognizing the potential of simulation to support reflective, skills-based training. A minority, 13% (95% CI 1%-25%) expressed a preference for alternative learning methods. Conclusions: VITAL-COMS was feasible to implement and acceptable to a diverse group of health care professionals. Participants demonstrated significant improvements in self-assessed communication skills and patient-scenario engagement. The simulation was perceived as realistic, emotionally engaging, and well-suited for training in sensitive conversations. These findings support further development and integration of VITAL-COMS into health education programs. Next steps include the translation of the insights identified in this study to inform a tool supported by generative artificial intelligence.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".