The Role of Cultural Values in the Folk Psychiatry Explanatory Framework: A Comparison of Chinese- and Euro-Canadians
Bibliographic record
Abstract
The Folk Psychiatry (FP) model proposes a process through which people understand mental illness, comprising four dimensions: pathologizing, moralizing, psychologizing, and medicalizing. Cultural group differences have been observed in previous research using part of this model, with one prior study suggesting that adherence to cultural values may partly explain these differences. The current study, therefore, evaluated whether horizontal–vertical and individualism–collectivism values contribute to explaining Chinese-Canadian (CC) versus Euro-Canadian (EC) cultural group differences among the FP dimensions. Undergraduate CC ( n = 252) and EC ( n = 296) students participated in an online survey, in which they read vignettes about a person exhibiting symptomatic behaviors of major depression. They were then asked about their impressions of the person’s behavior, based on FP scales. Our results show that CCs were more likely to pathologize and moralize the behaviors described in our study vignette, whereas ECs were more likely to employ psychologizing explanations. When compared with ECs, CCs were significantly more likely to endorse vertical individualism and vertical collectivism and less likely to endorse horizontal collectivism. There was an indirect effect of cultural group on moralizing through the endorsement of vertical (i.e., hierarchical) values. Our findings suggest that valuing social order and adherence to social norms may partly explain why some people view mental health problems as a personal fault.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".