Happily Ever After or Not? Marital Quality among Culturally Diverse Older-Aged Canadian Parents
Bibliographic record
Abstract
The quality of partnered relationships is integral to individual and family health and well-being over the life span. Significant shifts in ethno-cultural diversity, parental roles, and family life contribute to more complex partnership experiences in North American society. Drawing from a socio-cultural life course lens, we examine parental marital satisfaction/quality in later life in terms of ethnicity, socio-demographic variables (e.g., ethnic identity, gender, age, health status) and family context (e.g., presence of children at home, intergenerational relations, retirement status). Data are drawn from a sample of 454 married/partnered adults aged 50+ with a least one child aged 19–35 who reside in Metro Vancouver, British Columbia, from four cultural groups: British, Chinese, Persian/Iranian, and South Asian. Using Ordinary Linear Regression, we model predictors of three dependent variables: global marital satisfaction and two sub-scales, including positive and negative emotional/cognitive appraisals of relationship quality. Several ethnic group contrasts were supported, with Chinese reporting lower global marital satisfaction than the South Asians and Persian/Iranians reporting lower levels of positive marital appraisals than the South Asians. In addition, these associations were nuanced by interactions between ethnicity and gender, revealing distinct relationships with the dependent variables. Results also support associations for several covariates. In particular, greater income satisfaction and those reporting lower conflict with their children had higher marital quality; and males and those reporting better health only had positive associations with the global marital satisfaction scale. Implications for theorizing relationship quality in later life and recommendations for those who work with culturally diverse older adults (e.g., mental health care professionals, community service providers) are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".