Feasibility and Acceptability of a Mental Health Website for Adults With an Intellectual Disability: Qualitative Evaluation
Bibliographic record
Abstract
BACKGROUND: Electronic mental health (e-mental health) programs for people with an intellectual disability are currently underexplored but may provide a way of mitigating some of the barriers that this population faces in accessing appropriate mental health services. OBJECTIVE: The aim of this study was to examine the feasibility and acceptability of Healthy Mind, an e-mental health program for adults with an intellectual disability developed by the Black Dog Institute, focusing on the design and implementation of the website. METHODS: A qualitative research design was used, which involved semistructured interviews and focus groups with people with an intellectual disability, support workers, and allied health professionals. People with an intellectual disability were also observed while using the website. A thematic analysis was used to interrogate the interview transcripts and observational field notes. RESULTS: Participants found the content of the website informative and appreciated the many ways that the website had been made accessible to users. Participants voiced some differing requirements regarding the way information should be presented and accessed on the website. Acknowledging different types of support needs was identified as an important issue for website dissemination. CONCLUSIONS: The Healthy Mind website promises to provide an excellent tool for people with ID and their supporters. This research has pragmatic implications for the future development and implementation of the program, while contributing to knowledge in the broader fields of e-mental health and inclusive design for people with an intellectual disability.
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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 | 0.061 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".