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Record W2232116616 · doi:10.1089/apc.2014.0306

Implementing an Intimate Partner Violence (IPV) Screening Protocol in HIV Care

2015· article· en· W2232116616 on OpenAlexafffundabout
Sadaf E. Raissi, Hartmut B. Krentz, Reed Siemieniuk, M. John Gill

Bibliographic record

VenueAIDS Patient Care and STDs · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of TorontoAlberta Hip and Knee ClinicUniversity of Calgary
FundersCanadian Institutes of Health ResearchUniversity of Calgary
KeywordsDomestic violenceMedicineReferralFamily medicineSyndemicHealth carePoison controlSuicide preventionNursingPsychiatryHuman immunodeficiency virus (HIV)Medical emergency

Abstract

fetched live from OpenAlex

HIV and intimate partner violence (IPV) epidemics propagate and interact in a syndemic fashion contributing to excess burden of disease and poorer health outcomes. In order to understand the impact of IPV on HIV disease management, a universal screening program was implemented in the Southern Alberta Clinic in May 2009. We evaluated our IPV screening protocol and made recommendations for its usage in HIV care. IPV data obtained from patients were evaluated, supplemented with responses from a subset of in-depth interviews. 35% of 1721 patients reported experiencing IPV. Prevalence was higher among females (46%), Aboriginal Canadians (67%), bisexual male/females (48%), and gay males (35%). Of 158 patients interviewed, only 22% had previously been asked about IPV in any health care setting. Patients were responsive to routine IPV screening emphasizing that referral services need to be easily accessible. 23% of patients disclosing IPV subsequently connected to additional IPV resources after screening. We recommend that universal IPV screening be incorporated within regular HIV clinic care. The IPV survey should be given after trust has been established with regular follow-up every 6-12 months. A referral process to local agencies dealing with IPV must be in place for patients disclosing abuses.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.639

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 head0.044
GPT teacher head0.372
Teacher spread0.328 · 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 designQualitative
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

Citations23
Published2015
Admission routes3
Has abstractyes

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