Predictors of Mental Health Help-seeking Attitudes among Older Chinese Immigrants
Bibliographic record
Abstract
Chinese older adults are a growing population in Canada and data suggest that they are vulnerable to mental health issues. However, research has noted that older adults, especially ethnic minority seniors, underutilize mental health services. The influence of cultural factors on attitudes toward seeking mental health services may be a barrier to mental health utilization among Chinese older adults. Thus, the primary purpose of this study was to investigate a model of attitudes toward seeking mental health services among Chinese older adults. The secondary purpose of this study was to examine the relationships among mental health help-seeking attitudes, mental health utilization, and intentions to use mental health services among Chinese older adults. The current study assessed an adapted model (Kim, Atkinson, & Umemoto, 2001) of mental health help-seeking attitudes that included the predictors of personal factors (e.g., age, gender), environmental factors (e.g., social support), acculturation (participation in the host culture) and enculturation (maintenance of the heritage culture) among 149 middle-aged and elderly Chinese immigrants in Canada (Mage = 73.92 years; SD = 9.99; range: 55 to 95 years old). Although acculturation and enculturation were distinct processes among Chinese older adults, the model of help-seeking attitudes did not demonstrate good fit when evaluated using structural equation modelling. Acculturation and enculturation did not mediate the relationship among the predictors of age, gender, education, and perceived social support, and mental health help-seeking attitudes. More positive attitudes were evident among younger participants and respondents with better social support, but Chinese older adults had less positive attitudes than comparative samples. Attitudes were not related to seeking help from professionals (e.g. family physician), but were associated with seeking help from non-professionals (e.g., family and friends). In terms of mental health utilization, 8% of participants reported seeking help from a family physician, while 11% had sought help from non-professionals. This is the first study to apply the adapted model (Kim et al., 2001) to older Chinese adults and contributes a number of important findings, despite theoretical and methodological limitations inherent to cross-cultural research. The results of this study provide suggestions for future research and implications for practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".