Explanatory Models of Illness and Psychiatric Rehabilitation: A Clinical Sociology Approach
Bibliographic record
Abstract
The notion of explanatory models of illness (EMI) epitomizes the theme of social representation in social psychiatry. This article illustrates a clinical sociology approach to the subject by revisiting the seminal work of Kleinman and reflecting on the use of EMI in studying severe mental illnesses, particularly in China. A general literature review is provided to show the complexity of the subject, and the work of clinical sociologist Sévigny over the past two decades is summarized. A case analysis is conducted to illuminate the many social factors that came to play in affecting the experiences and perceptions of schizophrenic patients and their significant others in the nation’s capital Beijing in the 1990s. Diverse “explanations” in the experience of schizophrenia are explored, including the medical, the psychogenic, and the psychosocial models, among such others as inheritance and religious beliefs. Implications for research and clinical practice are discussed, including extending EMI study beyond illness interpretation to emphasize social rehabilitation.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.047 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".