Adherence to Typical Antipsychotics among Patients with Schizophrenia in Uganda: A Cross-Sectional Study
Bibliographic record
Abstract
Background: There has been a recent transition from typical to atypical antipsychotics in managing schizophrenia. This has been attributed to the acute side effects experienced by patients on typical antipsychotics that lead to nonadherence. However, the treatment cost with typical antipsychotics is cheaper (preferred in low-income settings), and there is no difference in the effectiveness, efficacy, discontinuation rate, or side effect symptom burden with atypical antipsychotics. This study is aimed at determining the prevalence of nonadherence and the associated factors to typical antipsychotics among patients with schizophrenia attending a psychiatric outpatient clinic at a rural tertiary facility in Uganda. Method: A cross-sectional study among 135 patients with schizophrenia for at least six months on typical antipsychotics (mean age of 39.7 (±11.9) and 55.6% were female) from a rural tertiary facility in Uganda. Data were collected regarding sociodemographics, adherence, insight for psychosis, attitude towards typical antipsychotics, side effects, satisfaction with medications, and explanations from health workers about medications and side effects. Logistic regression was used to determine the factors associated with nonadherence. Results: The prevalence of nonadherence was 16.3%, and the likelihood of being nonadherent was more among the poor (monthly earning below the poverty line). However, having reduced energy was associated with reducing the likelihood of having nonadherence. Conclusion: The prevalence of nonadherence was lower than many previously obtained prevalence and was comparable to nonadherence for atypical antipsychotics. However, to reduce nonadherence, we need all stakeholders (such as the government, insurance companies, and caregivers) to assist patients living in poverty with access to medication.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".