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Record W1987364174 · doi:10.2174/1874613601105010102

Sustainability of an HIV PEP Program for Sexual Assault Survivors: “Lessons Learned” from Health Care Providers

2011· article· en· W1987364174 on OpenAlexafffundabout
Janice Du Mont, Sheila Macdonald, Terri L. Myhr, Mona Loutfy

Bibliographic record

VenueThe Open AIDS Journal · 2011
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsOntario HIV Treatment NetworkUniversity of TorontoWomen's College Hospital
FundersCanadian Institutes of Health ResearchOntario Ministry of Health and Long-Term Care
KeywordsFocus groupMedicineNursingHealth careHuman immunodeficiency virus (HIV)CLARITYFamily medicinePolitical science

Abstract

fetched live from OpenAlex

This study explored challenges to continuing an HIV post-exposure prophylaxis (PEP) program of care provided to sexual assault survivors in the province of Ontario, Canada. Data were collected as part of an implementation and evaluation of a universal offering of HIV PEP (known as the HIV PEP Program) at 24 of 34 provincial hospital-based sexual assault treatment centres. Experienced health care providers were surveyed (n = 132) and interviewed in four focus groups (n = 26) about their perceptions of what, if any, factors threatened their ability to maintain the HIV PEP Program. All focus groups were audio-recorded and the recordings transcribed. The transcriptions and open-ended survey responses were analyzed using content analysis. Administrator, nurse, physician, social worker, and pharmacist respondents perceived important barriers to sustainability of the HIV PEP Program. Eight constructs were identified within four broad themes: resources (inadequate funds, overworked and unacknowledged staff), expertise (insufficient external supports, insufficiently trained and knowledgeable staff), commitment (lack of institutional support, physician resistance to offering HIV PEP), and accommodation (lack of flexibility in addressing specific client and community needs, inaccessibility and lack of clarity of tools). We discuss the implications of these findings and the actions that were taken to address the challenges.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.801
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.144
GPT teacher head0.443
Teacher spread0.299 · 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

Citations22
Published2011
Admission routes3
Has abstractyes

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