Drug Attitude, Insight, and Patient’s Knowledge About Prescribed Antipsychotics in Schizophrenia: A Cross-Sectional Survey
Bibliographic record
Abstract
Nobuhiro Nagai, 1–3 Hideaki Tani, 1, 4 Kazunari Yoshida, 1, 5 Philip Gerretsen, 6, 7 Takefumi Suzuki, 8 Saeko Ikai-Tani, 1, 9 Masaru Mimura, 1 Hiroyuki Uchida 1 1Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan; 2Department of Psychiatry, Minami-Hanno Hospital, Saitama, Japan; 3Department of Psychiatry, Tokyo-Kaido Hospital, Tokyo, Japan; 4Kimel Family Translational Imaging-Genetics Laboratory, Centre for Addiction and Mental Health, Toronto, ON, Canada; 5Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; 6Multimodal Imaging Group - Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; 7Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada; 8Department of Neuropsychiatry, University of Yamanashi Faculty of Medicine, Yamanashi, Japan; 9Physical Activity and Mental Health, Faculty of Kinesiology & Physical Education, University of Toronto, Toronto, ON, CanadaCorrespondence: Hiroyuki UchidaDepartment of Neuropsychiatry, Keio University School of Medicine, 35, Shinanomachi, Shinjuku-Ku, Tokyo 160-8582, JapanTel +81353633829Fax +81353790187Email hiroyuki.uchida.hu@gmail.comIntroduction: 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\n
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".