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Record W3002119713 · doi:10.1177/2055102919897384

Exploring pathways from violence and HIV disclosure without consent to depression, social support, and HIV medication self-efficacy among women living with HIV in Metro Vancouver, Canada

2020· article· en· W3002119713 on OpenAlexafffundabout
Kathleen Deering, Melissa Braschel, Carmen H. Logie, Flo Ranville, Andrea Krüsi, Putu Duff, Kate Shannon

Bibliographic record

VenueHealth Psychology Open · 2020
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of TorontoUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsStigma (botany)Psychological interventionHuman immunodeficiency virus (HIV)Social supportPsychiatryClinical psychologyInformed consentDepression (economics)Self-efficacyPsychologyMedicineFamily medicineAlternative medicinePsychotherapist

Abstract

fetched live from OpenAlex

We used path analysis to investigate complex pathways from HIV status disclosure without consent, physical/verbal violence and depression, social support, and HIV medication self-efficacy through mediators of HIV stigma among women living with HIV in Canada. In the final model, internalized stigma fully mediated the relationship between physical/verbal violence and reduced medication self-efficacy. Enacted stigma fully mediated the relationship between HIV status disclosure without consent and depression. Internalized stigma (β = 0.252; p < 0.001) had a significant negative direct effect on medication self-efficacy. Enacted stigma had a significant direct effect on depression (β = 0.162; p = 0.037). Findings will help improve services and interventions to promote quality of life and well-being of women 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)0.000
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 score0.362
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.093
GPT teacher head0.380
Teacher spread0.287 · 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

Citations8
Published2020
Admission routes3
Has abstractyes

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