Suicidality among inpatients who absconded from a tertiary mental health facility in Uganda: a retrospective study
Bibliographic record
Abstract
BACKGROUND: Suicidality and absconding from psychiatric care are two critical phenomena that complicate mental health care in developing countries. The aim of this study was twofold. First, to determine the prevalence of suicidality among absconders over two decades. Secondly, we set out to determine overall factors that influence the likelihood of having suicidal behaviors among absconders, as well as factors specific to each diagnosis. METHODS: This was a retrospective chart review of files of patients who absconded from inpatient psychiatric care at a tertiary psychiatric facility in southwestern Uganda between 2000 and 2020. A pre-tested electronic questionnaire was used for data abstraction of sociodemographic characteristics, documented suicidality, and other clinical variables. Data cleaning and analysis were conducted using STATA V.17. Logistic regression was performed for factors associated with suicidality. RESULTS: Among the absconders, 9.5% exhibited suicidality. Factors that heightened the odds of suicidality among absconders included being divorced or separated (adjusted odds ratio [aOR] = 2.00, 95% Confidence Interval [CI]: 1.20-3.31, p = 0.007), having depression (aOR = 5.41, 95% CI: 2.47-11.82, p < 0.001), a history of substance use (aOR = 1.50, 95% CI: 1.01-2.23, p = 0.049), and experiencing violence before hospitalization (aOR = 1.83, 95% CI: 1.14-2.94, p = 0.013). In contrast, substance use disorder (aOR = 0.25, 95% CI: 0.10-0.62, p = 0.003) and having schizophrenic spectrum disorders (aOR = 0.35, 95% CI: 0.18-0.68, p = 0.002) were linked to a decreased likelihood of suicidality among those who absconded. CONCLUSION: This study reveals a high burden of suicidality among individuals who abscond, with important risk factors such as marital status, depression, and a history of experiencing violence. It was noted that substance use disorder and schizophrenia spectrum disorders are associated with a reduced suicide risk. This study shows a significant interplay between clinical and demographic factors in predicting suicidality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".