Usability evaluation with mental health professionals and young people to develop an Internet-based cognitive-behaviour therapy program for adolescents with anxiety disorders
Bibliographic record
Abstract
BACKGROUND: Use of the Internet to deliver cognitive behavioural therapy, a frontline treatment for anxiety disorders, is emerging as an option to increase access to treatment among adolescents with anxiety disorders. This study examined the usability of the Internet-based component of Breathe, a CBT program designed for adolescents with mild to moderate anxiety and impairments. METHODS: A mixed-method usability testing design with semi-structured interviews, task completion, and survey by trained usability moderators was undertaken with two interactive cycles to determine the usability (ease of use, efficiency, errors, and user satisfaction) of the user interface and content areas of the program. Purposeful sampling was used to recruit mental health clinicians with expertise in treating adolescent anxiety disorders and young people aged 15 to 24 years involved. Testing involved using Web-conferencing software that allowed remote participation through personal computers. Two testing cycles involved participants completing structured 'think aloud' and 'cognitive walkthrough' tasks within the program. At the end of each cycle participants completed a 15-item global usability evaluation survey and were asked a series of open-ended questions. Descriptive and simple content analyses were used to identify and score usability issues for frequency and severity. RESULTS: Five clinicians and four young people (all < 20 years of age) participated. Most participants described their computer skills as 'good' (60% clinicians, 50% young people), and attitudes toward Internet-based health care ranged from negative (75% young people) to positive (60% clinicians, 25% young people). Scores from the global usability evaluation after both testing cycles ranged from 3.5 to 5 out of 5 in strong agreement/support of the program in terms of user performance indicators (i.e., learnability, efficiency and number of errors) and user satisfaction. Participants were able to complete all critical tasks with minimal errors. Errors and issues identified during testing were predominantly around enhancements to the visual design and navigational support. Opinions across usability elements did not differ between young people and clinician participants. CONCLUSIONS: A multi-method remote usability approach provided the opportunity to improve the technical interface, therapeutic messaging and user experience of an Internet-based treatment program for adolescent anxiety disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".