Dual harm among patients attending a mental health unit in Uganda: a hospital based retrospective study
Bibliographic record
Abstract
BACKGROUND: Dual harm encompasses the complex interplay of the co-occurrence of self-harm and aggression. Individuals with dual harm may display a more hazardous pattern of harmful behaviors like homicide-suicide compared to people with sole harm. This study aimed to examine the presence of dual harm among general psychiatry inpatients in a mental health unit in Uganda. METHODS: A retrospective chart review of 3098 inpatients from January 2018 to December 2021. Dual harm reported experience at admission was based on experiences of self-harm with harm to people or property or both. Logistic regression assessed the association between dual harm and sociodemographics and clinical characteristics. RESULTS: A total of 29 (1%) patients experienced dual harm, with five having experienced self-harm with both harm to others and property, 23 with harm to people, and one with harm to property. Dual harm was statistically significantly associated with the male gender at bivariate analysis. However, there were no statistically significant factors associated with dual harm at multivariate analysis or sensitivity analysis with the specific types of dual harm. CONCLUSION: General psychiatry inpatients in Uganda experience dual harm before admission at lower prevalence than in previous literature. However, no investigated sociodemographic and clinical factors could explain these experiences. Further studies looking at dual harm are warranted to understand these unfortunate experiences with serious consequences among patients in Uganda.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".