Absconding among admitted patients with bipolar affective disorder diagnosis in Uganda
Bibliographic record
Abstract
BACKGROUND: Hospitalization is often necessary for individuals with Bipolar affective Disorder (BAD) during severe manic or depressive episodes, as well as for stabilizing treatment regimens. However, a significant proportion of patients admitted for treatment of BAD abscond or leave the hospital without permission during their stay. In addition, patients managed for BAD may have unique characteristics that might force them into absconding. For example, the high prevalence of co-morbid substance use disorder - craving to use substances, suicidal behaviors - attempts to die by suicide, and cluster B personality disorders - characterized by impulsive acts. It is, therefore, essential to understand the factors contributing to absconding among patients with BAD, to facilitate designing strategies for preventing and managing this behavior. METHOD: This study was based on a retrospective chart review of the inpatients diagnosed with BAD at a tertiary psychiatry facility in Uganda from January 2018 to December 2021. RESULTS: Approximately 7.8% of those with BAD absconded from the hospital. The likelihood of absconding among those with BAD increased with the use of cannabis [adjusted odds ratio (aOR) = 4.00, 95% confidence interval (CI) = 1.22-13.09, p-value = 0.022] and having mood lability [aOR = 2.15, 95% CI = 1.10-4.21, p-value = 0.025]. However, receiving psychotherapy during the admission (aOR = 0.44, 95 CI = 0.26-0.74, p-value = 0.002) and treatment with haloperidol (aOR = 0.39, 95% CI = 0.18-0.83, p-value = 0.014) reduced the likelihood of absconding. CONCLUSION: Absconding among patients with BAD is common in Uganda. Those with symptoms of affective lability and those with comorbid cannabis use tend to abscond more, while those who receive haloperidol and psychotherapy are less likely to abscond.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".