Smart Technology in the Home for People Living in the Community with Mental Illness and Physical Comorbidities
Bibliographic record
Abstract
Abstract This study evaluated a smart technology intervention in the home as a support for individuals with severe mental illness. This study recruited 13 participants in a variety of community-based homes. Participants were offered a smartphone, a touchscreen monitor and health devices such as smartwatches, weigh-scales, and automated medication dispensers. Data was exported to the Lawson Integrated DataBase for care providers to monitor/track. Interviews with participants and focus groups with participants and care providers were conducted at baseline, 6-months and 12-months, and survey instruments were used to collect quantitative data about different dimensions of health and social determinants. Descriptive statistics from these outcome measures are presented as the sample size was too small for meaningful statistical inference. Qualitative analyses revealed a high degree of acceptability of the devices and motivation for healthy living, communication and mental health. Health Care Providers also noted improvements to client health. This study proves the feasibility of deploying smart technologies to support individuals with severe mental illness. Future scale-up would further our understanding of their impacts.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".