Command Hallucinations among Asian Patients with Schizophrenia
Bibliographic record
Abstract
OBJECTIVES: The impact of command hallucinations on patients and the determinants of patients' compliance with them are still poorly understood. The extant literature is also divided on their association with violence. This study aimed to establish the prevalence of command hallucinations and to identify the factors that affect compliance with the commands, together with patients' coping methods. METHODS: We recruited 50 consecutive male and 50 consecutive female schizophrenia inpatients who reported hearing voices in the 6 months prior to admission. We interviewed these patients, using a semistructured questionnaire. We collected information on the contents of their auditory hallucinations and their coping methods. RESULTS: Of the patients, 53 (53%) reported command hallucinations. Of these 53 patients, 58% were women and 48% were men; 62% reported complying with the commands. They were also more likely to comply with nonviolent commands. A history of self-harm predicted compliance. Those patients who did not comply with the commands adopted various methods of coping, of which praying was the most common. CONCLUSION: Command hallucinations are common in patients with schizophrenia. Patients with a history of self-harm need closer monitoring because they may be more likely to comply with these hallucinations. Assessment should also include the patient's own coping strategies, which can be incorporated into the treatment.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".