Quantitative Questions, Qualitative Answers: \nThe Cultural Meaning of Externally Oriented Thinking in a Chinese Psychiatric Sample
Bibliographic record
Abstract
Adaptive emotional norms are frequently assumed, and often go unexamined, when emotional constructs originating from the West are exported to other cultural contexts. The current study uses a mixed-methods design to examine alexithymia, whose underlying normative assumptions may be leading to over-pathologization and psychometric difficulties in cross-cultural usage. The externally oriented thinking (EOT) component of alexithymia, measured by the 20-Item Toronto Alexithymia Scale (TAS-20), shows well-recognized poor psychometric properties and potentially different but adaptive normative levels in non-English samples. In this two-part study, I first replicate and demonstrate past findings of poor internal consistency and weak model fit of the EOT subscale of the TAS-20 in a Chinese clinical sample (N = 276). To explain the quantitative observations, I then conduct a thematic analysis of audio-recorded interview responses using a subset of the sample (n = 23), who were administered the Toronto Structured Interview for Alexithymia. The qualitative results reveal that Chinese respondents demonstrated EOT tendencies such as organizing and analyzing experiences based on factual attributes, as well as engaging in more consideration of norms and less mentalization of feelings than normatively expected for Euro-Canadians. These findings provide explanations for EOT measurement issues from both task and conceptual levels. Integrating cultural emotion theories, I also consider the relative adaptiveness of EOT in service of specific cultural goals while challenging the appropriateness of its original ‘Western’ pathological assumptions. Implications of current findings for clinical practise, acculturative adjustment, and future empirical studies of attention to emotions are discussed.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".