Student–senior isolation prevention partnership: a Canada-wide programme to mitigate social exclusion during the COVID-19 pandemic
Bibliographic record
Abstract
Amidst the pandemic, Canada has taken critical steps to curb the transmission of the 2019 novel coronavirus disease (COVID-19). A key intervention has been physical distancing. Although physical distancing may protect older adults and other at-risk groups from COVID-19, research suggests quarantine and isolation may worsen mental health. Among older adults, social exclusion and social safety nets are social determinants of health (SDOH) that may be uniquely affected by the COVID-19 physical distancing measures. Health promotion programmes designed to reduce social exclusion and enhance social safety nets are one way to mitigate the potential mental health implications of this pandemic. The Student-Senior Isolation Prevention Partnership (SSIPP) is a student-led, community health promotion initiative that has scaled into a nation-wide effort to improve social connection among older adults. This initiative began with in-person visits and transformed into a tele-intervention guided by health promotion principles due to COVID-19. SSIPP continued to target the SDOH of social exclusion and social safety nets by pairing student volunteers with older adults to engage in weekly phone- and video-based interactions. Informed by the community partnership model by Best et al., SSIPP is built on the three orientations of empowerment, behaviour and organization, which are achieved through cross-disciplinary collaboration. This article reviews the importance of the adaptability of health promotion programmes, such as SSIPP during a pandemic, placing an emphasis on the lessons learned and future steps.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".