Kokoro no Kenko: Understanding Mental Health Beliefs from a Culturally Grounded Perspective Using a Mixed-Methods Approach in Japan and Canada
Bibliographic record
Abstract
Culture plays a crucial role in shaping how people perceive, interpret, and navigate psychological suffering. This dissertation examines cultural variations in mental health beliefs within Japan and Canada, utilizing two mixed-methods research designs. The overarching objective is to engage in interdisciplinary and culturally grounded research practices, driven by the need to address the lack of diversity, inclusion, and global perspectives in psychological science, commonly referred to as the “WEIRD” problem. These research practices entail critically reflecting on the generalizability of Western biomedical models, conducting literature reviews in Japanese, and fostering collaborations with Japanese researchers. \nManuscript 1 examines the differences in causal and help-seeking beliefs about mental illnesses between Japanese and Euro-Canadian students. In this study, content analysis revealed themes related to social-contextualization and unique cultural perspectives, such as filial piety and resting. Statistical analysis showed group differences in the endorsement of explanatory models across various conditions, including depression, autism spectrum disorder, schizophrenia, alcohol use disorder, and hikikomori. Overall, Japanese students tended to psychologize and recommend social support, whereas Euro-Canadian students tended to medicalize and recommend medication and self-care. \nManuscripts 2 and 3 apply cultural consensus theory to explore shared beliefs about mental health, depression, and therapeutic alliance among Japanese clinical psychologists. Using a two-phase sequential mixed-methods design, cultural domain analysis identified salient terms reflecting mental health issues and changes in licensure within Japan’s socio-cultural and historical context. Cultural consensus analysis demonstrated shared models for most domains, with exceptions in for beliefs about an incompetent clinician, a difficult client, and external barriers. \nThis dissertation makes a valuable contribution by exploring culturally distinctive mental health beliefs and advocating for the benefits of mixed-methods approaches. It addresses the limitations of the contemporary psychological literature, which predominantly relies on theories, sampling, and methods prevalent in Western (i.e., “WEIRD”) contexts. These studies are proposed as an initial stride towards developing culturally grounded models for clinical assessment and care, catering to the needs of people from non-Western cultural backgrounds. The findings carry important implications for mental health research, policy, community care, practice, and education, especially in multicultural contexts.
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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 | 0.016 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".