myHealthHub for older adult inpatients to reduce loneliness, and improve patient engagement and mental health: protocol of a pilot randomized controlled trial
Bibliographic record
Abstract
Objectives Older Canadian adults make up 85% of hospital stays which are associated with increased loneliness, stress, anxiety, and/or depression. There is a need for novel approaches to reduce loneliness and mental health outcomes in older adult hospital inpatients to prevent further strain on an already overwhelmed healthcare system.Methods This is a pilot randomized controlled trial (RCT) exploring the efficacy of a bedside multimodal interaction system, myHealthHub, on loneliness, quality of life (QOL), patient engagement, and other mental health outcomes compared to an active control group in older adult inpatients (n = 60) from baseline to 5-days. Qualitative analyses will be conducted through semi-structured interviews with older adults (n = 8-10) and hospital staff, nurses, and clinicians (n = 4-5) facilitating the service to evaluate patient engagement and experience with myHealthHub.Results Not applicable.Conclusion This novel pilot clinical trial will obtain preliminary data on the efficacy of myHealthHub in reducing loneliness, QOL, patient engagement, and mental health outcomes in older adult inpatients. If successful, this could provide a potential means to improve patient experience in hospitals and reduce the burden and additional expense on the healthcare system.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.051 | 0.007 |
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".