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Record W2899137408 · doi:10.1080/20008198.2018.1536286

Prevalence and correlates of probable post-traumatic stress disorder and common mental disorders in a population with a high prevalence of HIV in Zimbabwe

2018· article· en· W2899137408 on OpenAlexfundno aff
Ruth Verhey, L.J. Gibson, Jonathan Brakarsh, Dixon Chibanda, Soraya Seedat

Bibliographic record

VenueEuropean journal of psychotraumatology · 2018
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsnot available
FundersGrand Challenges Canada
KeywordsMedicineDepression (economics)PsychiatryComorbidityOdds ratioAnxietyConfidence intervalPopulationNational Comorbidity SurveyChecklistCross-sectional studyClinical psychologyInternal medicinePsychologyEnvironmental health

Abstract

fetched live from OpenAlex

Background: We investigated the prevalence of and factors associated with post-traumatic stress disorder (PTSD) and common mental disorders (CMDs), which include depression and anxiety disorders, in a setting with a prevalence of high human immunodeficiency virus (HIV) within a primary care clinic, using the PTSD Checklist for DSM-5 and the 14-item Shona Symptom Questionnaire, both locally validated screening tools.Methods: A cross-sectional survey was carried out with adult patients (n = 204) from the largest primary care clinic facility in Harare, Zimbabwe, in June 2016.Results: A total of 83 patients (40.7%) met the criteria for probable PTSD, of whom 57 (69.5%) had comorbid CMDs. Among people living with HIV, 42 (55.3%) had PTSD. Probable PTSD was associated with having experienced a negative life event in the past 6 months [adjusted odds ratio (OR) 3.73, 95% confidence interval (CI) 1.49–9.34] or screening positive for one or more CMD (adjusted OR 6.48, 95% CI 3.35–2.54).Conclusion: People living with HIV showed a high prevalence of PTSD and CMD comorbidity. PTSD screening should be considered when the CMD screen is positive and there is a history of negative life events.

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)0.001
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 score0.013
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.022
GPT teacher head0.315
Teacher spread0.293 · 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

Citations29
Published2018
Admission routes1
Has abstractyes

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