“It Makes You Feel Good to Help!”: An Exploratory Study of the Experience of Peer Mentoring in Long-Term Care
Bibliographic record
Abstract
Abstract Social isolation and loneliness in long-term care settings are a growing concern. Drawing on concepts of social citizenship, we developed a peer mentoring program in which resident mentors and volunteers formed a team, met weekly for training, and paired up to visit isolated residents. In this article, we explore the experiences of the resident mentors. As part of a larger mixed-methods study conducted in 10 sites in Canada, we interviewed mentors (n = 48) and analysed data using inductive thematic analysis. We identified three inter-related themes: Helping others, helping ourselves described the personal benefits experienced through adopting a helping role; Building a bigger social world encapsulated new connections with those visited, and; Facing challenges, learning together described how mentors dealt with challenges as a team. Our findings suggest that a structured approach to mentoring benefits residents and helps them feel confident taking on a role supporting their isolated peers.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".