"Excessive Thinking" as Explanatory Model for Schizophrenia: Impacts on Stigma and "Moral" Status in Mainland China
Bibliographic record
Abstract
Although psychiatric stigma in China is particularly pervasive and damaging, rates of high expressed emotion ("EE" or family members' emotional attitudes that predict relapse) are generally lower than rates found in Western countries. In light of this seemingly incongruous juxtaposition and because Chinese comprise approximately one-fifth of the world's mentally ill, we examine how one of the most widely held causal beliefs of schizophrenia--excessive thinking (xiang tai duo)--may powerfully shape how those exhibiting psychotic symptoms pass from "normal" status to stigmatized "other." Using a framework by which stigma threatens an actor's capacity to participate in core everyday engagements, we examine how expressions of excessive thinking intersect with psychotic symptoms and how this idiom reduces stigma by preserving essential moral standing. Four focus groups with family members (n = 34 total) of schizophrenia outpatients, who had participated in psychoeducation, were conducted in Beijing. Open coding was conducted by 2 bilingual coders achieving high interrater agreement. Common expressions of excessive thinking--taking things too hard that is perceived as a causal factor and unwarranted suspicion that is used to benignly interpret paranoid symptoms encapsulated disruptive behaviors that closely overlapped with psychotic symptoms. Because excessive thinking is understood to occur universally, this idiom encourages socially accommodating behavior that signifies acceptance of these individuals as full-status community members. In contrast, due to beliefs implying moral contamination, those labeled mentally ill are threatened with both subtle and outright social exclusion. We discuss implications of this idiom for EE and the detection of schizophrenia "prodrome" in China.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".