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Record W4409700070 · doi:10.1186/s12888-025-06865-6

Prevalence of aggression and associated factors among inpatients with mental illness at tertiary hospitals in Southwestern Uganda

2025· article· en· W4409700070 on OpenAlexaff
Badru Kayongo, Godfrey Zari Rukundo, Alain Favina, Samuel Maling

Bibliographic record

VenueBMC Psychiatry · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMental illnessAggressionPsychiatryTertiary careMedicinePsychologyClinical psychologyMental healthFamily medicine

Abstract

fetched live from OpenAlex

BACKGROUND: In mental health treatment facilities around the world, aggression aimed towards medical personnel and other patients is a serious issue. Types of aggression include; verbal aggression, aggression towards property, self-harm/auto-aggression, and physical aggression. Studies show 1 in 5 patients admitted to acute mental health care wards in high-income countries commit an act of physical violence during admission. In Uganda, there is limited literature on aggression among patients with mental illness admitted in psychiatric wards in our setting. This study aimed to investigate prevalence, types and associated factors of aggression among patients with mental illness admitted at tertiary hospitals in southwestern Uganda. METHODS: This was a cross-sectional study that involved 280 participants from four tertiary hospitals in southwestern Uganda. Aggression was assessed using the Modified Overt Aggression Scale. The sociodemographic and clinical characteristics were collected using a structured sociodemographic questionnaire. The prevalence of aggression was assessed using proportions and the associated factors were assessed using the multinomial logistic regression analysis. RESULTS: The prevalence of severe aggression was 42.9% with verbal aggression being the most common type while auto aggression was the least common. Involuntary admission and having a personal history of aggression were associated with all the 3 levels of aggression: mild, moderate and severe aggression, while having a positive family history of mental illness was associated with two levels of aggression (moderate and severe aggression). Having a history of substance use was only associated with moderate aggression and having history of childhood abuse was associated with severe aggression. Coming from a rural area was associated with severe aggression. CONCLUSION: The prevalence of aggression is high among patients with mental illness admitted at tertiary hospitals in southwestern Uganda with verbal aggression being the commonest. Mental health professionals should pay attention to aggression among patients bearing in mind that people admitted involuntary, with personal history of aggression have a high likelihood of presenting with aggression. The mental health treatment facilities should consider adopting more of voluntary admission for patients presenting with aggression and only utilize involuntary admission when it is absolutely necessary and in line with the rights and responsibilities for patients with mental illness.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.264
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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