Factors associated with non-adherence to clinic visits among patients with severe mental illness enrolled in the SMILE study in Uganda
Bibliographic record
Abstract
BACKGROUND: Non-adherence to clinic visits among patients with severe mental illness (SMI) presents challenges to patient management, treatment outcomes, and research in resource-limited settings. This study investigated the factors associated with non-adherence to clinic visits in Uganda, using appointment attendance as a proxy for clinic adherence. METHODS: This cohort study took place at Butabika National Referral Mental Hospital and Masaka Regional Referral Hospital from January to March 2018. A total of 1,201 participants with confirmed diagnoses of SMI were systematically sampled from over 3,000 outpatients. Data on socio-demographic, psychosocial, psychiatric, and behavioural factors were collected, with adherence defined as attending scheduled visits at 3, 6, 9, and 12 months post-enrolment. Descriptive statistics, bivariate, and multivariate logistic regression analyses were employed to identify significant predictors of non-adherence. RESULTS: The overall prevalence of non-adherence to clinic visits was 20% (95% CI: 17.8 − 22.3%), with males showing higher rates (22.9%) compared to females (17.6%). Factors significantly associated with increased non-adherence included younger age, being treated at Butabika National Referral Mental Hospital, and alcohol use. Conversely, higher social support was linked to improved adherence. Among psychiatric variables, patients with major depressive disorder and severe psychiatric symptoms were more likely to miss appointments. CONCLUSIONS: The study highlights the multifaceted nature of non-adherence in patients with SMI, emphasizing the need for targeted interventions addressing socio-demographic, psychosocial, and clinical factors. Enhancing social support, managing psychiatric symptoms, and reducing substance use are critical strategies for improving adherence rates, which could, in turn, lead to better health outcomes and resource optimization in mental health services.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".