The Impact of Peer Mentoring on Loneliness, Depression, and Social Engagement in Long-Term Care
Bibliographic record
Abstract
Loneliness, depression, and social isolation are common among people living in long-term care homes, despite the activities provided. We examined the impact of a new peer mentoring program called Java Mentorship on mentees’ loneliness, depression, and social engagement, and described their perceptions of the visits. We conducted a mixed-methods approach in 10 homes in Ontario, Canada, and enrolled residents as mentees ( n = 74). We used quantitative surveys and qualitative interviews to understand their experience. After 6 months, mentees ( n = 43) showed a 30% reduction in depression ( p = .02, d = .76), a 12% reduction in loneliness ( p = .02, d = .76), and a 60% increase in the number of monthly programs attended ( p = .01, d = .37), with small-to-medium effect sizes. The analysis of mentee’s interviews revealed positive perceptions. This program offers an innovative, nonpharmacological alternative to the treatment of loneliness and depression.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".