Japanese Clinical Psychologists' Consensus Beliefs about Mental Health: A <scp>Mixed‐Methods</scp> Approach
Bibliographic record
Abstract
Abstract This study applied a two‐phase, mixed‐methods research design, grounded in cultural consensus theory (CCT), to examine shared beliefs about mental health held by Japanese clinical psychologists (CPs). In CCT, qualitative methods are first used to identify culturally salient elements of a domain; factor analysis is then used to quantify the degree of sharedness, an approach known as cultural consensus analysis (CCA). First, a free‐listing technique with 16 Japanese CPs was conducted to elicit salient terms for the two domains: (a) how members of the general public acquire beliefs about mental health; and (b) how Japanese mental healthcare ought to be reformed. In the second phase, CCA was conducted through a survey completed by 100 CPs. The free‐listing analysis generated 21 and 23 culturally salient terms for the two domains, respectively. Then, CCA demonstrated that the two domains could each be characterized as a single cultural model with a high degree of consensus. CCT provides a systematic mixed‐methods approach that is particularly well‐suited to investigating culturally grounded shared beliefs held by people in a specific cultural context.
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 | 0.081 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".