Multinational comparative cross-sectional survey of views of medical students about acceptable terminology and subgroups in schizophrenia
Bibliographic record
Abstract
AIM: The aim of this study was to inform thinking around the terminology for 'schizophrenia' in different countries. OBJECTIVES: The objective of this study was to investigate: (1) whether medical students view alternative terminology (psychosis subgroups), derived from vulnerability-stress models of schizophrenia, as acceptable and less stigmatising than the term schizophrenia; (2) if there are differences in attitudes to the different terminology across countries with different cultures and (3) whether clinical training has an impact in reducing stigma. DESIGN: This is a cross-sectional survey that examined the attitudes of medical students towards schizophrenia and the alternative subgroups. SETTING: The study was conducted across eight sites: (1) University of Southampton, UK; (2) All India Institute of Medical Science, India; (3) Rowan University, USA; (4) Peshawar Medical College, Pakistan; (5) Capital Medical University, China; (6) College of Medicine and Medical sciences, Bahrain; (7) Queens University, Kingston, Canada and (8) University of Cape Town, South Africa. METHOD: This study extended an initial pilot conducted by the Royal College of Psychiatrists on the term schizophrenia and psychosis subgroups to assess whether the subgroup terminology might have an effect on the attitudes of a convenience sample of medical students from eight different countries and potentially play a role in reducing stigmatisation. RESULTS: 1873 medical students completed a questionnaire recording their attitudes to schizophrenia and the psychosis subgroups. A reduction in negative perceptions were found for the psychosis subgroups, especially for the stress sensitivity psychosis and anxiety psychosis subgroups. Negative perceptions were found for drug-related psychosis. Participants who had undergone clinical training had overall positive attitudes. Differences across different countries were found. CONCLUSION: The attitudes towards psychosis subgroups used in this study have shown mixed results and variation across countries. Further research is warranted to investigate acceptability of terminology. Methods of reducing stigma are discussed in line with the findings. ETHICS: The study received ethical approval from ERGO (Ethics and Research Governance Online; ID: 15972) and subsequently from the ethics committee at each site.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".