Use of Mobile and Computer Devices to Support Recovery in People With Serious Mental Illness: Survey Study
Bibliographic record
Abstract
BACKGROUND: Mental health recovery refers to an individual's experience of gaining a sense of personal control, striving towards one's life goals, and meeting one's needs. Although people with serious mental illness own and use electronic devices for general purposes, knowledge of their current use and interest in future use for supporting mental health recovery remains limited. OBJECTIVE: This study aimed to identify smartphone, tablet, and computer apps that mental health service recipients use and want to use to support their recovery. METHODS: In this pilot study, we surveyed a convenience sample of 63 mental health service recipients with serious mental illness. The survey assessed current use and interest in mobile and computer devices to support recovery. RESULTS: Listening to music (60%), accessing the internet (59%), calling (59%), and texting (54%) people were the top functions currently used by participants on their device to support their recovery. Participants expressed interest in learning how to use apps for anxiety/stress management (45%), mood management (45%), monitoring mental health symptoms (43%), cognitive behavioral therapy (40%), sleep (38%), and dialectical behavior therapy (38%) to support their recovery. CONCLUSIONS: Mental health service recipients currently use general functions such as listening to music and calling friends to support recovery. Nevertheless, they reported interest in trying more specific illness-management apps.
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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 | 0.000 |
| 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".