The differential effects of social and ethnocultural factors on depression and anxiety in Laotian older adults
Bibliographic record
Abstract
The objectives of the study was to explore the differential effects of social and ethnocultural factors (independent variables) on depression and anxiety (dependent variables) never before studied specific refugee cohort. Participants were 58 females and 41 males with a Laotian background living in Southwestern Ontario area. Their median age was 67 years with a range from 55 to 94 years. A Hierarchical Multiple Linear Regression technique was used to test the explanatory power of social versus ethnocultural determinants of depression and anxiety (after controlling for life events). Results indicated that the social variables used in the stress-process model derived from research of mainstream communities applied to depression but not to anxiety. One third of the variance in depression (controlling for life events) was explained by income and social support. Variance in anxiety was not explained by income and social support explained only 9% of variance in anxiety. However, ethnic identity, family allocentrism, and ingroup activities, contributed an additional 11% to the variance in anxiety. Ethnocultural specific variables appeared to be more relevant to the understanding of individual differences in the experience of anxiety.
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 | 0.000 | 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 | 0.001 | 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) | 0.001 | 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".