Challenges and Strategies to Adapt the Provision of Support Services to Older Adults and Caregivers During the COVID-19 Pandemic: The Perspective of Community Organizations
Bibliographic record
Abstract
This study documented the provision of services and issues experienced by community organizations supporting older adults and caregivers in the province of Quebec during the coronavirus disease (COVID-19) pandemic, as well as promising strategies to adapt the provision of services in this context. A cross-sectional electronic survey using open- and closed-ended questions was conducted in July 2020. Almost three-quarters of the 307 respondents (71.4%) reported having maintained services at least partially throughout the lockdown, and the majority (85.3%) adapted their services. Among key challenges, participants reported difficulties identifying and supporting older adults at greater risk of vulnerability (54.8%), managing health risks for service users (60.2%), and recruiting volunteers (59.5%). Promising strategies included strategies to reach out to older adults and understand their needs (e.g., systematic phone calls) in addition to direct interventions supporting them (e.g., activities promoting social ties); implementing prevention and protection measures; accessing and using technologies; human resources management (e.g., recruiting new volunteers); finding financial support for their organization; developing intersectoral partnerships (e.g., multisectoral crisis cell); and promoting a positive view of older adults. The integration of multiple perspectives from different stakeholders may help identify strategies potentially transferable to other crises in order to meet older adults' needs.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".