Virtual volunteering, community support, and self-care in Chinese communities in Canada
Bibliographic record
Abstract
Abstract Objective: Our study aimed to explore the link between environment and health, driven by a virtual volunteering program, and discuss the implications of virtual volunteering in community support in the postpandemic era. Methods: Using a purposive sampling strategy, we recruited 21 participants with diverse backgrounds, including age and sex. They participated in individual interviews of 1–2 hours, all of which followed a semistructured interview guide centered on topics, such as volunteer experiences, impacts, and understanding of volunteering. The audio recordings were transcribed verbatim and analyzed using a grounded theory approach. Results: Our data showed that interpersonal interactions through the virtual volunteering program helped participants deal with loneliness, and boosted their mental health. The social network provided participants with social support. Moreover, activities, such as dancing, yoga, Tai Chi, and singing, facilitated physical health. Participants not only learned various skills but also served as mentors, through which they increased self-efficacy through reciprocal role transformation. Conclusions: Our study concludes that virtual volunteer programs have a positive impact on people’s physical and mental health. The participants demonstrated different levels of resilience when their environments changed. By situating virtual volunteering as the center of people’s health, our findings suggest that people gain informational, instrumental, and emotional support through virtual volunteering. Future research should examine the experiences of individuals from other ethnic groups and settings to supplement this study.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".