EXPLORING THE IMPACT OF AN EBOOK CLUB PROGRAM ON APATHY AMONG OLDER CANADIANS IN LONG-TERM CARE: A FEASIBILITY STUDY
Bibliographic record
Abstract
Abstract Apathy is a common and persistent problem among older people in institutional settings. Research has shown that apathy can lead to rapid decline in cognitive status, serious functional impairments and decrease in the life span of those affected, yet it is under-researched, and under-managed. The aim of this study was to explore ways by which apathy can be mitigated in this population using eReader technology during book club program implementation. We recruited participants from various long-term care facilities (N=18) and each participants took part in semi-structured interviews, and self-reported apathy assessment both at the start and end of the program. We tracked engagement in the eBook club and time commitment to reading using a logbook. The findings suggest that older people, including persons with mild to moderate dementia, are open to adopting new technologies and the use of eReaders during book club programs is feasible for long term care facilities. Overall, we found that the use of eReader technology is a safer and more effective way of delivering book club program to older people in long term care facilities particularly during pandemic such as COVID 19 as they are easy to disinfect in comparison to physical books. We recommend that long term care staff should adapt eReaders into their existing book club or literacy programs and consider utilizing it as a strategy to prevent or manage apathy in 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".