Feasibility and Acceptability of an Interactive Mental Well-Being Intervention for People With Intellectual Disabilities: Pilot Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: The availability of both digital and traditional mental well-being interventions is rising, but these interventions typically do not consider people with intellectual disabilities as potential users. OBJECTIVE: The study aimed to explore the acceptability and feasibility of a new digital intervention, developed with and for people with intellectual disabilities, to improve their subjective well-being. METHODS: Using a single-group pre-post design, participants with intellectual disabilities and their caregivers completed the 4-week intervention. Mixed methods questionnaires assessed the acceptability of the intervention, in addition to self-report and proxy-report measures of subjective well-being and behavioral problems. RESULTS: A total of 12 men with mild to moderate intellectual disabilities enrolled in and completed the study alongside 8 caregivers. Participant acceptability of the intervention was high, and feedback covered multiple aspects of the intervention, including (1) program concept and design, (2) program content, and (3) intervention usage. Self-rated mood barometers indicated mood improvements for 5 participants, deteriorations for 2 participants, and no observed changes for the remaining participants. Statistical analyses yielded no difference from pretest (median=79; range 39-86) to posttest (median=79; range 21-96) for subjective well-being in people with intellectual disabilities (W=10.5; P=.17) and for behavioral problems (W=14; P=.05). CONCLUSIONS: People with intellectual disabilities and their caregivers are receptive to using digital well-being interventions, and this research shows such interventions to be feasible in routine practice. Given the acceptability of the intervention, its potential efficacy can now be evaluated in people with intellectual disabilities and symptoms of reduced mental well-being.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".