Factors Influencing Attitudes Towards Seeking Professional Help Among East and Southeast Asian Immigrant and Refugee Women
Bibliographic record
Abstract
BACKGROUND AND AIMS: It has been recognized that Asian immigrants in North America have lower rates of mental health service utilization. From the perspective of cross-cultural psychiatry, one of the most important cultural factors may be differences in the explanatory model of illness. This article examines the relationship of causal beliefs, perceived service accessibility and attitudes towards seeking mental health care. METHOD: The sample consisted of 1000 immigrant and refugee women from five ethnic minority communities in Toronto, including three Chinese Canadian communities (Hong Kong, mainland China and Taiwan), Korean Canadians and Vietnamese Canadians. Data were acquired by a self-administered structured questionnaire. Quantitative data were analysed using MANOVA, ANOVA and stepwise multiple regression. RESULTS: The five ethnic minority groups of women differed in their explanatory models about mental illness and distress. In the full model where other variables were controlled for, the most significant factor predicting attitudes towards seeking professional help was perceived access for all groups except the Hong Kong Chinese. In the last group, those subscribing more to a Western stress model of illness had a more positive attitude towards seeking professional help, while those subscribing more to supernatural beliefs had a more negative attitude. Age and education were not significant predictors. CONCLUSION: Perceived access is one of the main factors that influence attitudes toward seeking professional help. Explanatory models may predict help-seeking behaviours if perceived access to such services is available.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".