<p>Drug Attitude, Insight, and Patient’s Knowledge About Prescribed Antipsychotics in Schizophrenia: A Cross-Sectional Survey</p>
Bibliographic record
Abstract
Introduction: While patients’ perspectives toward pharmacotherapy are expected to be directly influenced by their motivation and understanding of the treatment that they are currently receiving, no study has comprehensively investigated the impact of insight into illness and knowledge for the ongoing pharmacotherapy on the attitude towards drug treatment among patients with schizophrenia. Materials and Methods: One hundred forty-eight Japanese outpatients diagnosed with schizophrenia, according to the International Classification of Diseases 10th edition, were included (mean±SD age, 47.3± 12.4 years; 90 men (60.8%)). Attitudes toward antipsychotic treatment and insight into illness were assessed with the Drug Attitude Inventory-10 (DAI-10) and the VAGUS, respectively. In addition, a multiple-choice questionnaire that was designed to examine patients’ knowledge about therapeutic effects, types, and implicated neurotransmitters of antipsychotic drugs they were receiving was utilized. Results: The mean±SD of DAI-10 score was 4.7± 4.2. The multiple regression analysis found that lower Positive and Negative Syndrome Scale (PANSS) scores, higher VAGUS scores, and longer illness duration were significantly associated with higher DAI-10 scores (β=− 0.226, P =0.009; β=0.250, P =0.008; β=0.203, P =0.034, respectively). There was a significant difference in the DAI-10 scores between the subjects who gave more accurate answers regarding the effects of their primary antipsychotic and those who did not (mean±SD, 5.57± 4.38 vs 4.13± 4.04, P =0.043); however, this finding failed to survive the multiple regression analysis. Conclusion: Better insight into illness and treatment, lower illness severity, longer illness duration, and possibly greater knowledge about the therapeutic effects of medications may lead to better attitudes towards pharmacotherapy among patients with schizophrenia, which has an important implication for this typically chronic mental condition requiring long-term antipsychotic treatment to sustain stability. Keywords: drug attitude, adherence, insight, knowledge, schizophrenia, antipsychotic
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".