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Record W2324449452 · doi:10.1177/2325957414536228

Gender Differences in Severity and Correlates of Depression Symptoms in People Living with HIV in Ontario, Canada

2014· article· en· W2324449452 on OpenAlexafffundabout
Kinda Aljassem, Janet Raboud, Trevor Hart, Anita C. Benoit, DeSheng Su, Shari L. Margolese, Sean B. Rourke, Sergio Rueda, Ann N. Burchell, John Cairney, Paul A. Shuper, Mona Loutfy

Bibliographic record

VenueJournal of the International Association of Providers of AIDS Care (JIAPAC) · 2014
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsSt. Michael's HospitalUniversity of TorontoToronto Metropolitan UniversityUniversity Health NetworkMcMaster UniversityOntario HIV Treatment NetworkWomen's College HospitalPublic Health Ontario
FundersOntario HIV Treatment Network
KeywordsDepression (economics)Human immunodeficiency virus (HIV)PsychiatryPsychologyClinical psychologyGerontologyMedicineDemographySociologyVirology

Abstract

fetched live from OpenAlex

This study investigates the differences in severity and correlates of depression symptoms among 1069 men and 267 women living with HIV in Ontario, Canada, who completed the 20-item Center for Epidemiologic Studies Depression Scale (CES-D). Women had higher CES-D scores than that of men (median [interquartile range]: 13 [5-26] versus 9 [3-20], P=.0004). More women had total CES-D scores>15 (mild-moderate depression; 44% versus 33%, P=.002) and >21 (severe depression; 31% versus 23%, P=.003). Unlike men, at age 40, women's scores increased yearly (0.4 per increased year, P=.005). The distribution of scores differed by gender: There was no difference in the 10th percentile of depression scores, 0 (95% confidence interval [CI]: 1.0-1.0) but the 75th percentile of depression scores for women was 6 (95% CI: 2.0-10.0) points higher than that of men. Important gender differences exist in depression symptoms and in correlates of symptoms in people living with HIV.

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)0.002
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 score0.554
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.008
GPT teacher head0.237
Teacher spread0.229 · 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

Citations47
Published2014
Admission routes3
Has abstractyes

Explore more

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