THE IMPACT OF PEER MENTORING IN RESIDENTIAL CARE ON THOSE VISITED
Bibliographic record
Abstract
Depression and loneliness are biopsychosocial determinants of health, which contribute to functional decline and mortality among older adults living in residential care. Research in other settings indicates peer support may be effective at reducing depression and loneliness and enhancing social identity. Therefore, an innovative peer mentoring intervention was developed based on the social identity theory in which volunteers and residents (mentors) form a supportive team. Mentors meet weekly, receive education and then external volunteers pair up with resident volunteers to visit socially isolated residents (visitees). This pilot study was conducted to explore visitees’ experiences with the intervention and perceived outcomes. Data were collected for 74 visitees in 10 homes in Ontario, Canada. The effectiveness of the program was assessed over a 6-month period using qualitative interviews and quantitative outcome measures including standardized measures of depressive symptoms and loneliness. Attendance at other programs was also monitored. During their interviews visitees described strong emotional connections with their peers, which contributed to feelings of empowerment and an interest in becoming mentors themselves. Visitees reported reduced symptoms of depression (p = 0.02) and loneliness (p = 0.02), and a 60% increase in the number of other monthly programs attended was observed (p = ≤ 0.01). The findings of this pilot suggest that peer mentorship may be a promising means of reducing symptoms of depression and loneliness, which are extremely prevalent in these settings. These findings will inform revisions to the program that will be evaluated in future research examining the efficacy of this intervention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".