Acceptance and Use of eHealth in Support and Psychological Therapy for People With Intellectual Disabilities: Two Cross-Sectional Studies of Health Care Professionals
Bibliographic record
Abstract
Background Acceptance of health care professionals is of paramount importance for the uptake and implementation of eHealth. The Unified Theory of Acceptance and Use of Technology (UTAUT) model is a widely used framework for studying health care professionals’ acceptance and actual use of eHealth among general client populations. However, there is limited understanding of the eHealth acceptance of health care professionals working with people with intellectual disabilities (ID). Objective This study aimed to explore the applicability of the UTAUT model toward understanding the acceptance, intention to use, and actual use of eHealth among support staff and therapists working with people with ID. Methods A total of 2 cross-sectional survey studies were conducted among health care professionals from 5 health care organizations for people with ID in the Netherlands in 2018 (n=311) and in 2021 during the COVID-19 pandemic (n=326). In addition to confirmatory and exploratory factor analyses to evaluate both the original UTAUT model and an extended version, descriptive analysis was used to explore participants’ characteristics, acceptance levels, and eHealth usage. Moderator analysis and multiple regression analysis were also used. Results A confirmatory factor analysis indicated a poor fit for both the original 4-factor UTAUT model and the extended version. An exploratory factor analysis was then conducted, resulting in a more satisfactory 5-factor model after removing 1 item with a factor loading <.40. Internal consistency of the 5 factors ranged from acceptable to good (Cronbach α=.76-.85). Collectively, all factors predicted the intention to use eHealth in 2018 (R2=0.47; F5,305=54.885; P<.001) and in 2021 (R2=0.43; F5,320=49.32; P<.001). Participants scored moderately on all 5 acceptance factors in both 2018 and 2021. Moderator analysis indicated that age and voluntariness influence the relationship between factors that determined acceptance and intention to use eHealth. Conclusions The findings from 2 cross-sectional studies conducted in 2018 and 2021, using an extended UTAUT model, gave a deeper understanding of eHealth acceptance among health care professionals who work with people with ID.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".