A Virtual Reality Resident Training Curriculum on Behavioral Health Anticipatory Guidance: Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: Behavioral health disorders have steadily increased and been exacerbated by the COVID-19 pandemic. Though behavioral health disorders can be successfully mitigated with early implementation of evidence-based parent management strategies, education for pediatric residents on behavioral health anticipatory guidance has been limited to date, with training challenges compounded by the physical distancing requirements of the COVID-19 pandemic. Virtual reality (VR) simulations provide an opportunity to train residents on this complex competency by allowing deliberate practice of necessary skills while adhering to current social distancing guidelines. OBJECTIVE: This study explored the usability of a VR-based behavioral health anticipatory guidance curriculum for pediatric residents. METHODS: This mixed methods study included 14 postgraduate third-year pediatric residents who completed the behavioral health anticipatory guidance VR curriculum. Residents completed the MEC Spatial Presence Questionnaire to assess immersion in the virtual environment. Semistructured interviews were used to elucidate residents' perspectives on the curriculum's content and format. The interviews were analyzed using conventional content analysis. RESULTS: Quantitatively, residents reported a high degree of immersion, spatial presence, and cognitive involvement. Most residents (11/14, 79%) agreed or strongly agreed that it seemed as though they took part in the action of the simulation. Qualitatively, two themes emerged from the data: (1) the curriculum expands behavioral health anticipatory guidance and motivational interviewing knowledge and skills and (2) VR technology is uniquely positioned to develop competence. These themes revealed that the curriculum expanded their current level of knowledge and skill, addressed training gaps, and was applicable to all residents. Additionally, residents experienced VR as immersive, feasible, realistic to the clinic setting, and a safe space to practice and learn new skills. CONCLUSIONS: Pilot data indicates that VR may be an effective tool to teach pediatric residents behavioral health anticipatory guidance, meeting a current gap in medical education training. This VR curriculum is particularly relevant in the context of the COVID-19 pandemic given the increased behavioral health concerns of families.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".