Social Isolation of Older Adults, Family, and Formal Caregivers During the COVID-19 Pandemic: Stories and Solutions Through Participatory Action Research
Bibliographic record
Abstract
This participatory action research (PAR) aimed to understand the health implications of guidelines impacting social isolation among frail community-dwelling older adults and their family and formal caregivers during the coronavirus disease (COVID-19) pandemic. Reflexive thematic analysis (RTA) of data collected from 10 policy/procedural documents revealed four themes: valuing principles, identifying problem(s), setting priorities, and making recommendations. Interviews with 31 participants from Peterborough, Ontario, also revealed four themes: sacrificing social health, diminishing physical health, draining mental health, and defining supports. Recommendations to decision makers were finalized at a knowledge exchange event involving participants and members of Age-friendly Peterborough. Key findings demonstrate the need for Canadian governments and health and social service agencies to enhance access to technology-based interventions, and educational and financial resources for caregivers. Meaningful communication and collaboration between older adults, caregivers, and decision makers are also needed to reduce the gap between policy and practice when addressing social isolation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.027 | 0.021 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| 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".