“We see each other from a distance”: Neighbourhood social relationships during the COVID-19 pandemic matter for older adults’ social connectedness
Bibliographic record
Abstract
INTRODUCTION: We extend previous research to illustrate how individual, interpersonal and neighbourhood factors in a high-density urban setting in Vancouver, Canada, shape social connectedness experiences of community-dwelling older adults during the first wave of the COVID-19 pandemic. METHODS: We conducted 31 semi-structured interviews and collected objective measures of loneliness and social connectedness (surveys). RESULTS: Three dimensions of the neighbourhood environment influenced social connectedness: (i) interactions with neighbours, (ii) involvement with neighbourhood-based organizations, and (ii) outdoor pedestrian spaces. Seventy-one percent of participants felt a strong sense of belonging to their local community, while 39% were classified as high or extremely lonely. SUMMARY: Many participants leveraged pre-existing social ties to maintain connections during the pandemic. However, volunteer outreach was vital for more isolated older adults. Although many participants felt lonely and isolated at times, the relative ease and accessibility with which they could connect with others in their neighbourhood environment, may have helped mitigate persistent loneliness. CONCLUSION: Strategies that foster social connectedness over the longer term, need to prioritize the needs of older adults who face multiple barriers to equitable social participation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".