Explanatory model of illness of the patients with schizophrenia and the role of educational intervention
Bibliographic record
Abstract
This randomized controlled trial was conducted at Department of Psychiatry, Lady Reading Hospital, Peshawar from February to August 2015 to explore beliefs and concepts of patients with schizophrenia about their illness and to find out the effectiveness of structured educational intervention in changing the explanatory models of illness of the patients and in their symptoms reduction. One hundred and three patients were recruited in the trial who were randomly assigned to two groups i.e., Experimental (n=53) and Control i.e., Treatment As Usual, TAU (n=50). Intervention was applied to experimental group only, once a month for three months. Short Explanatory Model Interview (SEMI), Brief Psychiatric Rating Scale (BPRS), Positive And Negative Syndrome Scale (PANSS), Global Assessment of Functioning (GAF) and Compliance Rating Scale were applied on all patients at baseline and at 3months follow up. Scores on PANSS (Total), BPRS and GAF showed improvement in the experimental group as compared to TAU group, at follow up, with the p values of 0.000, 0.002 and 0.000, respectively. On follow up, 44 (95.6%) patients of experimental group achieved complete compliance as compared to 17 (47.2%) patients of TAU group [p=0.000]. On baseline analysis of SEMI, in the experimental group, only 3.8% (n=2) knew about name of the illness, which increased to 54.3% (n=25) on follow up, while in TAU group it improved to 5.6% (n=2) as compared to 0% at baseline (p=0.000). The result suggest that Structured educational intervention can be effective in modifying the beliefs of the patients regarding their illness.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".