Patients’ Knowledge about Prescribed Antipsychotics and Medication Adherence in Schizophrenia: A Cross-Sectional Survey
Bibliographic record
Abstract
Abstract Introduction Data on the knowledge about antipsychotic medications prescribed in patients with schizophrenia are very limited. Moreover, it remains unclear how patients’ knowledge about prescribed antipsychotics affects medication adherence. Methods ighty-one Japanese outpatients with schizophrenia according to the International Classification of Diseases, 10th edition, were included. Patients’ knowledge of the primary antipsychotics prescribed to them in terms of therapeutic effects, type, and implicated neurotransmitters was assessed with a multiple-choice questionnaire developed for this study. Medication possession ratios (MPRs) were compared between patients who answered correctly and those who did not in each category. Results The percentages of subjects who answered correctly regarding antipsychotic effects, type, and implicated neurotransmitters were low at 30.9%, 30.9%, and 7.4%, respectively. No differences were found in MPRs between subjects who answered correctly and those who did not. Discussion Our preliminary results indicate that patients lack knowledge about their antipsychotic medications. More concerning, they suggest that knowledge about prescribed antipsychotics may not directly translate into actual medication adherence in patients with 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".