Personalized Tablets for Residents in Long-Term Care to Support Recreation and Mitigate Isolation
Bibliographic record
Abstract
OBJECTIVES: There is a digital divide in long-term care homes (LTCHs), with few residents having regular access to internet-connected devices. In this study, we provided long-term care residents with personalized and adapted tablets. We aimed to understand what factors influenced tablet use and the impact of tablet access on opportunities for social connection and recreation. DESIGN: A pragmatic, mixed-methods multicenter, open-label, uncontrolled interventional study with assessment of outcomes at baseline and 3 months. SETTING AND PARTICIPANTS: A total of 58 resident-care partner dyads were recruited across 7 LTCHs in Ontario, Canada. The main inclusion criterion was having a care partner willing to participate, and we excluded residents who already had an internet-connected device. METHODS: Resident demographics, functional status assessments, and recreational engagement were captured using items from the Resident Assessment Instrument/Minimum Data Set. Care partners completed a questionnaire about relational closeness and site leads assessed resident quality of life before and approximately 3 months after tablet distribution. Interviews with 23 care partners and 7 residents post-implementation were completed and analyzed. RESULTS: The median tablet use by participants was 7 minutes (interquartile range 27) per day on average over the study period. Predictors of higher tablet use were younger age, higher cognitive functioning, absence of hearing impairment, and having a care partner who lives farther away. There was no improvement on quantitative measures of quality of life, recreation, or relational closeness. In interviews, participants identified many different opportunities afforded by access to personalized tablets. CONCLUSIONS AND IMPLICATIONS: Some LTCH residents without current access to the internet benefit from being provided a personal tablet and use it in a variety of ways to enrich their lives. There is a critical need to bridge the digital divide for this population.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".