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Record W3173794630 · doi:10.1097/nnr.0000000000000524

Acceptability of Nurse-Driven HIV Screening for Key Populations in Emergency Departments

2021· article· en· W3173794630 on OpenAlexaff
Judith Leblanc, José Côté, Patricia Auger, Geneviève Rouleau, Théophile Bastide, Hélène Piquet, Hélène Fromentin, Carole Jegou, Gaëlle Duchêne, Rachel Verbrugghe, Cécile Lancien, Tabassome Simon, Anne‐Claude Crémieux

Bibliographic record

VenueNursing Research · 2021
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsKey (lock)Human immunodeficiency virus (HIV)Medical emergencyMedicineNurse practitionersHIV screeningNursingFamily medicineHealth careComputer scienceMen who have sex with menPolitical scienceComputer security

Abstract

fetched live from OpenAlex

BACKGROUND: Optimizing care continuum entry interventions is key to ending the HIV epidemic. Offering HIV screening to key populations in emergency departments (EDs) is a strategy that has been demonstrated to be effective. Analyzing patient and provider perceptions of such screening can help identify implementation facilitators and barriers. OBJECTIVES: The aim of this study was to investigate the acceptability of offering nurse-driven HIV screening to key populations based on data collected from patients, nurses, and other service providers. METHODS: This convergent mixed-methods study was a substudy of a cluster-randomized two-period crossover trial conducted in eight EDs to evaluate the effectiveness of the screening strategy. During the DICI-VIH (Dépistage Infirmier CIblé du VIH) trial, questionnaires were distributed to patients aged 18-64 years. Based on their responses, nurses offered screening to members of key populations.Over 5 days during the intervention period in four EDs, 218 patients were secondarily questioned about the acceptability of screening. Nurses completed 271 questionnaires pre- and posttrial regarding acceptability in all eight EDs. Descriptive analyses were conducted on these quantitative data. Convenience and purposeful sampling was used to recruit 53 providers to be interviewed posttrial. Two coders conducted a directed qualitative content analysis of the interview transcripts independently. RESULTS: The vast majority of patients (95%) were comfortable with questions asked to determine membership in key populations and agreed (89%) that screening should be offered to key populations in EDs. Nurses mostly agreed that offering screening to key populations was well accepted by patients (62.2% pretrial and 71.4% posttrial), was easy to implement, and fell within the nursing sphere of competence. Pretrial, 73% of the nurses felt that such screening could be implemented in EDs. Posttrial, the proportion was 41%. Three themes emerged from the interviews: preference for targeted screening and a written questionnaire to identify key populations, facilitators of long-term implementation, and implementation barriers. Nurses were favorable to such screening provided specific conditions were met regarding training, support, collective involvement, and flexibility of application to overcome organizational and individual barriers. DISCUSSION: Screening for key populations was perceived as acceptable and beneficial by patients and providers. Addressing the identified facilitators and barriers would help increase screening implementation in EDs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.031
metaresearch head score (Gemma)0.070
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.031
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.070
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.213
GPT teacher head0.528
Teacher spread0.314 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2021
Admission routes1
Has abstractyes

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