Family and Patient Predictors of Symptomatic Status in Schizophrenia
Bibliographic record
Abstract
OBJECTIVE: To test an interactive hypothesis that, in schizophrenia, a combination of patients' and relatives' characteristics at 1-month postdischarge from hospital (Time 1 [T1]) better predicts the level of psychotic symptoms at follow-up (Time 2 [T2]), than do the characteristics of patients or relatives alone. METHODS: Male patients (n = 38) with a diagnosis of schizophrenia, without substance abuse, and in contact with their families, were recruited at the time of hospital discharge. Patients' psychotic symptom levels were monitored every 2 weeks until follow-up, while family measures were administered at T1 and T2. The 4 predictor variables in the regression analysis were T1 symptom levels of the patient and 3 measures of family interaction (expressed emotion, family burden, and family functioning). RESULTS: The model based on the family variable, family burden at T1, and the patient variable, patients' remitted levels of psychotic symptoms at T1, was found to significantly predict the level of psychotic symptoms at T2. These 2 T1 variables made independent and additive contributions to the level of psychotic symptoms at T2, predicting 19% of the variance. Neither expressed emotion nor family functioning at T1 added to the prediction. CONCLUSION: This finding suggests a patient-family interactional component to symptomatic relapse in schizophrenia.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".