Acceptability of Nurse-Driven HIV Screening for Key Populations in Emergency Departments
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".