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Record W2309229824 · doi:10.1155/2016/3015468

Burden of Suicidal Ideation and Attempt among Persons Living with HIV and AIDS in Semiurban Uganda

2016· article· en· W2309229824 on OpenAlexafffund
Godfrey Zari Rukundo, Brian L. Mishara, Eugene Kinyanda

Bibliographic record

VenueAIDS Research and Treatment · 2016
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversité du Québec à Montréal
FundersMedical Research CouncilMbarara University of Science and TechnologyUniversité du Québec à Montréal
KeywordsSuicidal ideationMedicineAngerPsychiatryDepression (economics)AnxietyHuman immunodeficiency virus (HIV)Stigma (botany)Suicide preventionPoison controlImmunologyMedical emergency

Abstract

fetched live from OpenAlex

Although the impact of HIV/AIDS has changed globally, it still causes considerable morbidity and mortality, including suicidality, in countries like Uganda. This paper describes the burden and risk factors for suicidal ideation and attempt among 543 HIV-positive attending two HIV specialized clinics in Mbarara municipality, Uganda. The rate of suicidal ideation was 8.8% (n = 48; 95% CI: 6.70-11.50) and suicidal attempt was 3.1% (17, 95% CI 2.00-5.00). The factors associated with increased risk for suicidal ideation and attempts were state anger (OR = 1.06, 95% CI: 1.03-1.09; p = 0.001); trait anger (OR 1.10, 95% CI 1.04-1.16, p = 0.002); depression (OR 1.13, 95% CI 1.07-1.20, p = 0.001); hopelessness (OR 1.12, 95% CI 1.02-1.23, p = 0.024); anxiety (OR 1.06, 95% CI 1.03-1.09); low social support (OR 0.19, 95% CI 0.07-0.47, p = 0.001); inability to provide for others (OR 0.19, 95% CI 0.07-0.47, p = 0.001); and stigma (OR 2.48, 95% CI 1.11-5.54, p = 0.027). At multivariate analysis, only state anger remained statistically significant. HIV/AIDS is associated with several clinical, psychological, and social factors which increase vulnerability to suicidal ideation and attempts. Making suicide risk assessment and management an integral part of HIV care is warranted.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.022
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

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

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 head0.051
GPT teacher head0.351
Teacher spread0.300 · 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 teacher head, 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

Citations44
Published2016
Admission routes2
Has abstractyes

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