Improving immigrants' physical and mental health through volunteering
Bibliographic record
Abstract
With one fifth of the total population being immigrants, Canada has been one of the most attractive countries as a destination for immigration. Normally, immigrants who have greater human capital and better health are invited to migrate. As a result, most immigrants have better health than their local-born counterparts, which is known as the healthy immigrant effect. However, such effect gradually dissipates, partially due to a lack of social capital. Studies suggest that social capital and immigrants‘ sense of belonging can be enhanced through volunteering by immigrants. Both volunteering and sense of belonging are important social determinants for one‘s health status. This study aims to examine the relationships between volunteering, sense of belonging, and health among immigrants in Canada. It is hypothesized that volunteering positively predicts immigrants‘ physical and mental health and these associations are mediated by sense of belonging. Utilizing a population-based data, 6784 foreign-born participants were selected. Variables such as volunteering, sense of community belonging, physical and mental health as well as other control variables (education, age, visible minority status, income, years residing in Canada, importance of religious and spiritual beliefs, and sex) were used. Two mediation analyses were then conducted. The results revealed that not only does volunteering have a positive prediction on immigrants‘ physical and mental health, it also positively predicts their sense of belonging. Simultaneously, sense of belonging serves as a mediator on the pathways between volunteering and both physical as well as mental health. Such pathways were found significant after taking the control variables into account. This finding validates the significance of volunteering as a civic engagement activity in its association with immigrants‘ integration in Canadian society and their health. It is pivotal to utilize immigrants‘ strengths and empower them to participate socially, culturally, economically, and politically in society. Volunteering by immigrants appears to be a way to achieve this participation. This thesis sheds light on volunteering programs for social workers and human service professionals to instil immigrants‘ sense of community belonging. Ultimately, it can help build immigrants‘ social capital and uphold their health statuses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".