Factors Affecting Usability and Acceptability of an Online Platform Used by Caregivers in Child and Adolescent Mental Health Services: Mixed Methods Study
Bibliographic record
Abstract
Background: Young people and families endure protracted waits for specialist mental health support in the United Kingdom. Staff shortages and limited resources have led many organizations to develop digital platforms to improve access to support. myHealthE is a digital platform used by families referred to Child and Adolescent Mental Health Services in South London. It was initially designed to improve the collection of routine outcome measures and subsequently the "virtual waiting room" module was added, which includes information about child and adolescent mental health as well as signposting to supportive services. However, little is known about the acceptability or use of digital resources, such as myHealthE, or about sociodemographic inequalities affecting access to these resources. Objective: This study aimed to assess the usability and acceptability of myHealthE as well as investigating whether any digital divides existed among its userbase in terms of sociodemographic characteristics. Methods: A survey was sent to all myHealthE users (N=7337) in May 2023. Caregivers were asked about their usage of myHealthE, their levels of comfort with technology and the internet. They completed the System Usability Scale and gave open-ended feedback on their experiences of using myHealthE. Results: A total of 680 caregivers responded, of whom 45% (n=306) were from a Black, Asian, or a minority ethnic background. Most (n=666, 98%) used a mobile phone to access myHealthE, and many had not accessed the platform's full functionality, including the new "virtual waiting room" module. Household income was a significant predictor of caregivers' levels of comfort using technology; caregivers were 13% more likely to be comfortable using technology with each increasing income bracket (adjusted odds ratio 1.13, 95% CI 1.00-1.29). Themes generated from caregivers' feedback highlight strengths of digital innovation as well as ideas for improvement, such as making digital platforms more personalized and tailored toward an individual's needs. Conclusions: Technology can bring many benefits to health care; however, sole reliance on technology may result in many individuals being excluded. To enhance engagement, clinical services must ensure that digital platforms are mobile friendly, personalized, that users are alerted and directed to their full functionality, and that efforts are made to bridge digital divides. Enhancing dissemination practices and improving accessibility to informative resources on the internet is critical to provide fair access to all using Child and Adolescent Mental Health Services.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".