Perceived causes of marital dissatisfaction among Nigerian immigrants in North America: A qualitative study
Bibliographic record
Abstract
Marital dissatisfaction among Nigerian immigrants in North America (NINA) arises from a complex interplay of cultural transitions, acculturation stress, and socio-economic pressures. This cross-sectional study followed a phenomenological approach and the guidelines from the Consolidated criteria for reporting qualitative research (COREQ) to examine the perceived causes of marital dissatisfaction among NINA through in-depth interviews with 15 participants residing in the United States and Canada. Participants were adults with at least one year of living with a Nigerian spouse in North America. Ten themes emerged from the data analysis identifying key marital challenges faced by Nigerian immigrant couples in North America. These include cultural conflicts between patriarchal and egalitarian values, financial stress, job insecurity, and extended family obligations. Other challenges involve social norms discouraging open discussions, experiences of abuse, lack of relationship skills, peer and societal pressures, infidelity, parenting conflicts, immigration-related stress, and an overreliance on prayer without practical interventions. These findings highlight the need for culturally sensitive support systems for this population and policies focusing on providing culturally tailored marital counseling, financial literacy programs, and accessible culturally sensitive mental health support services.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".