RESILIENCE IN JAPANESE OLDER IMMIGRANTS AND ROLES OF COMMUNITY SUPPORT DURING THE COVID-19 PANDEMIC
Bibliographic record
Abstract
Abstract The objective of this community-based study was to explore how Japanese older immigrants cultivated resilience in overcoming challenges during the COVID-19 pandemic and how Tonari Gumi, a community service agency, supported the process. As Japanese people make up a small proportion of the population in Canada, there are limited resources to meet their distinct needs. Thus, Japanese older adults were particularly affected by disrupted support and service systems when COVID-19 public health orders were implemented. In this qualitative study, seven community-dwelling Japanese older immigrants and five staff from Tonari Gumi participated in semi-structured interviews. The interviews were analyzed thematically using a conceptual lens of resilience, which refers to the ability to survive and thrive in the face of adverse life experiences. Our analysis yielded three themes: Challenges and concerns; Staying active: physically, mentally, and socially; and Creating needs-based services and programs to survive, connect, and enjoy. The initial challenge experienced by Japanese older immigrants was “a feeling of emptiness,” followed by hardships associated with digital literacy, English literacy, fear of COVID, and concerns about the future. In response to the challenges, Japanese older immigrants stayed active by rebuilding and sustaining regular exercise habits, nurturing and sustaining positive mindsets, and sustaining and expanding social connections. Tonari Gumi developed and delivered a variety of new services and programs to meet the needs of survival, social connection, and fun. We will discuss key actions that older individuals and service providers took on to facilitate resilience and implications for research, policy, and practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".