The use of virtual reality during medical procedures in a pediatric orthopedic setting: A mixed‐methods pilot feasibility study
Bibliographic record
Abstract
Medical procedures cause pain and anxiety in children. Distraction techniques, including virtual reality (VR), may be used in healthcare settings to reduce rates of undertreated procedural pain and anxiety. A mixed-methods, concurrent triangulation design was piloted at a pediatric orthopedic hospital to assess the feasibility, clinical utility, tolerability, and initial clinical efficacy of VR distraction during medical procedures received by patients with complex musculoskeletal conditions. Questionnaire, scale, interview, observation, and focus group data were collected from patients, their parents, and healthcare professionals. Triangulation of key quantitative and qualitative findings produced final themes and meta-themes. A total of 44 patients and their parents undergoing intravenous insertions (n = 30), pin removals (n = 7), blood draws (n = 3), Botox injections (n = 2), dressing change (n = 1), and urodynamic test (n = 1) were recruited along with 11 healthcare professionals performing the medical procedures. The following themes resulted from triangulation of data sources: VR intervention was (a) feasible because VR was easily implemented into the clinical workflow, (b) clinically useful as VR was accepted by stakeholders and easy to use, (c) tolerable as VR caused minimal discomfort, and (d) showed initial clinical efficacy in managing procedural pain and anxiety. These findings will inform policies and procedures for VR use in practice and a sustainable implementation across the [name of hospital removed for peer review] network.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".