Digital Community Inclusion of Individuals With Serious Mental Illness: A National Survey to Map Digital Technology Use and Community Participation Patterns in the Digital Era
Bibliographic record
Abstract
BACKGROUND: Despite the growing interest in developing and using mobile health (mHealth) and digital technologies in mental health, little is known about the scope and nature of virtual community inclusion. OBJECTIVE: The overarching goal of this study was to understand and conceptualize virtual community inclusion of individuals with serious mental illness (SMI). Specific objectives of this study were as follows: (1) mapping the prevalence, trends, and experiences related to mHealth and digital technology use among individuals with SMI; (2) comparing patterns of technology use by individuals with and those without SMI; and (3) examining whether use of mHealth and digital technologies predicts recovery among individuals with SMI. METHODS: A web-based survey of technology use and virtual participation was developed and distributed among adults with and those without SMI via social media, national email discussion lists, nonprofit organizations, and advocacy groups. RESULTS: A total of 381 adults aged 18 years or older participated in the survey, of whom 199 (52%) identified as having a SMI. Participants with SMI reported significantly greater access to technology and significantly fewer days of face-to-face participation in community activities than those without SMI. Among participants with SMI, greater technology use was positively associated with positive emotions and significantly predicted recovery. CONCLUSIONS: This study is the first to explore, map, and conceptualize virtual community inclusion among adults with SMI. Our findings indicate a gap in the literature and research on community inclusion and participation, and emphasize the need for virtual community inclusion, particularly during the COVID-19 pandemic and its future implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".