From culture to symptom: Testing a structural model of “Chinese somatization”
Bibliographic record
Abstract
"Chinese somatization" has been frequently discussed over the past three decades of cultural psychiatry, and has more recently been demonstrated in cross-national comparisons. Empirical studies of potential explanations are lacking, however. Ryder and Chentsova-Dutton (2012) proposed that Chinese somatization can be understood as a cultural script for depression, noting that the literature is divided on whether this script primarily involves felt bodily experience or a stigma-avoiding communication strategy. Two samples from Hunan province, China-one of undergraduate students (n = 213) and one of depressed psychiatric outpatients (n = 281)-completed the same set of self-report questionnaires, including a somatization questionnaire developed in Chinese. Confirmatory factor analysis demonstrated that Chinese somatization could be understood as two correlated factors: one focusing on the experience and expression of distress, the other on its conceptualization and communication. Structural equation modeling demonstrated that traditional Chinese cultural values are associated with both of these factors, but only bodily experience is associated with somatic depressive symptoms. This study takes a first step towards directly evaluating explanations for Chinese somatization, pointing the way to future multimethod investigations of this cultural script.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".