Evaluation of the Compliance, Acceptance, and Usability of a Web-Based eHealth Intervention for Parents of Children With Infantile Hemangiomas: Usability Study
Bibliographic record
Abstract
BACKGROUND: Infantile hemangiomas (IH) are common benign vascular tumors in children. Recognition and timely referral of high risk IH to specialized centers is important. This might be achieved by involving parents in the care for IH by means of an eHealth intervention. OBJECTIVE: The objective of our study was to evaluate parent compliance, acceptance, and usability of an open access, Web-based eHealth intervention (including e-learning and e-consult) designed to increase parents' knowledge and (risk) evaluation of IH. METHODS: A cross-sectional study of parents who completed the eHealth intervention between October 2010 and November 2012 was carried out. All parents were sent a study questionnaire. Questions to evaluate compliance (to the advice given by a dermatologist during e-consultation) were asked. Acceptance and usability were evaluated by using the modified Technology Acceptance Model. RESULTS: A total of 224 parents completed the eHealth intervention and received the questionnaire, 135/224 parents responded (response rate was 60.3%). There were 128/135 questionnaires that were completed and included. A total of 110/128 (85.9%) parents were compliant to the advice of the dermatologist. There were 116.8/128 (91.3%) that perceived the eHealth intervention as useful and almost all parents (98.4%, 126/128) found the information in the e-learning clear. There were 29/128 (22.7%) that experienced technical problems. The majority of the parents (94.5%, 121/128) found the eHealth intervention reliable and most of them (98.4%, 126/128) would recommend the eHealth intervention to other parents. Noncompliant parents judged the eHealth intervention significantly less reliable compared to compliant parents (71%, 10/14 versus 97.3%, 107/110; P=.003). CONCLUSIONS: Parents of children with an IH showed a high compliance (85.9%, 110/128) to the advice of the dermatologist given via our Web-based eHealth intervention. This high compliance might be positively influenced by the good acceptance and usability of the eHealth intervention and might result in timely presentation and treatment of children with high risk IH in specialized centers.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".