OVERCOMING APATHY AMONG OLDER COMMUNITY DWELLERS IN RURAL NORTHERN BC: THE BENEFITS OF ENGAGING IN AN EBOOK CLUB
Bibliographic record
Abstract
Abstract The study aimed to investigate the impact of an eBook Club on the apathy and overall well-being of older adults dwelling in rural community settings. Employing a mixed-method approach, the research gathered data through surveys and interviews both before and after the program implementation. We recruited twenty-eight older adults, aged between 60 and 86 years, predominantly female (23 out of 28) from three communities in Northern British Columbia, Canada. Each participant was provided with a Kobo eReader loaded with books tailored to their interests, alongside a tutorial for using the device. Each eReader was linked to the public library, facilitating easy access to a broader range of books. The eBook Club convened weekly for sessions lasting 45 minutes to an hour, with groups capped at four members, over a span of 4 to 6 weeks. Initially, about half of the participants (10 out of 28) reported apathy. Remarkably, by the program’s conclusion, these individuals unanimously reported a decrease in apathetic feelings. Additional benefits noted by participants included enhanced mood, improved social connections, and sustained engagement with reading activities beyond the club meetings. We concluded that a relatively straightforward intervention like the eBook Club can significantly ameliorate symptoms of apathy and bolster the overall well-being of older individuals in rural community settings. While initial challenges with the Kobo eReader were reported, the collective support and encouragement within the group played a crucial role in overcoming these obstacles, underscoring the value of peer support in facilitating the adoption of new technologies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".