E-learning, Distance Education, and VR/AR in Orthopedic Training: European Cross-sectional Survey of Trainee Acceptance Guided by TAM and UTAUT Framework (Preprint)
Bibliographic record
Abstract
BACKGROUND: Digital technologies increasingly shape postgraduate medical education, yet orthopedic and trauma training face unique challenges because of the tactile, procedurally focused skills involved. Digital tools partially address these needs, but gaps remain, particularly across diverse European contexts. OBJECTIVE: Our primary aim was to quantitatively assess predictors of digital learning technology acceptance (e-learning, distance education, and virtual reality [VR] and augmented reality [AR]) among European orthopedic and trauma trainees, drawing on the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT) as conceptual guides. Specifically, we examined how perceived usefulness and perceived ease of use (TAM), alongside performance expectancy, effort expectancy, social influence, and facilitating conditions (UTAUT), related to trainees' acceptance of digital technologies. These constructs guided variable selection and grouping, attitudinal scale design, and interpretation of how individual and contextual factors shape acceptance of digital learning tools in orthopedic training. Secondary aims were to describe adoption and attitude patterns, identify attitudinal trainee profiles, and examine contextual associations (eg, workplace type and national gross domestic product [GDP]). METHODS: We distributed a multinational survey via European trainee federations and used validated scales to assess digital competence and attitudes and gathered demographic data (n=217 across 29 European countries). We administered the questionnaire in English; however, respondents who self-reported English proficiency below the intermediate level were excluded from the analyses to minimize potential comprehension-related bias. The survey assessed digital experience, self-reported digital competence, and attitudes toward e-learning, distance education, and VR/AR, and collected detailed demographic and workplace data. Expert review, cognitive pretesting, and pilot testing ensured validity and clarity. Analytical methods included Wilcoxon tests, ANOVA, clustering, multinomial logistic regression, and factor analysis to ensure the reliability and validity of attitudinal measures. RESULTS: e-Learning technologies were the most widely adopted, whereas VR/AR tools were less frequently used despite high average attitude ratings (mean 4.07, SD 0.88). Cluster analysis identified 3 distinctive groups-enthusiastic, supportive, and hesitant-that differed significantly in digital competence and acceptance profiles. Digital competence and national GDP emerged as significant predictors of group membership, consistent with TAM/UTAUT expectations that perceived capability and contextual facilitating conditions shape acceptance. Variation in attitudes was further associated with workplace type and regional resource disparities, underscoring the influence of contextual factors on technology adoption. CONCLUSIONS: European orthopedic trainees show broad support for digital innovations, preferring VR/AR despite low use. Preliminary evidence supports digital competence as a key mediator of acceptance, with GDP and workplace disparities predicting profiles (hesitant vs enthusiastic). Competence-first strategies and targeted resource equity (eg, low-GDP subsidies), together with policy adjustments, may address regional disparities. Future longitudinal and multimethod studies are needed to test causal pathways implied by TAM and UTAUT and to evaluate the generalizability of these findings across specialties and educational contexts.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".