ETHNIC IDENTITY MODERATES THE RELATIONSHIP BETWEEN SOCIAL SUPPORT AND DEPRESSION OLDER CHINESE CANADIANS
Bibliographic record
Abstract
It is well-documented that perceived social support decreases the risks of depression in older adults. Past studies have also noted a negative relationship between ethnic identity and depression, especially among minority seniors. As the Canadian senior population becomes increasingly diverse, it is important to understand the interplay between ethnic identity, perceived social support, and depression. The current study examined the moderating role of the enculturation in the relationship between social support and depressive symptoms among 219 community-dwelling older Chinese Canadian immigrants (mean age = 73.41 years; 62% female). Individuals completed a set of questionnaires, including measures of perceived social support, social network size, depressive symptoms, and subjective enculturation and acculturation levels. Negative correlations were found between perceived social support and depression (r = -.29, p < .001), and enculturation and depression (r = -.15, p = .04). Conversely, neither social network size nor acculturation was correlated with depression (ps > .05). A bootstrapping moderation analysis revealed enculturation significantly moderated the relationship between perceived social support and depressive symptoms (β = -.49, p < .001). Specifically, Chinese older adults with medium and high levels of enculturation displayed a significantly enhanced protective effect of perceived social support on depressive symptoms. From these findings, it is postulated that older Chinese immigrants who maintain their cultural values may be more socially active by engaging in cultural activities that are congruent with their ethnic identity, which in turn may influence their sense of social support and reduce the likelihood of experiencing depressive symptoms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".