Implementation of Nurse‐Driven HIV Screening Targeting Key Populations in Emergency Departments: A Multilevel Analysis From the DICI‐VIH Trial
Bibliographic record
Abstract
BACKGROUND: In countries with concentrated HIV epidemics, optimizing screening to reach individuals with undiagnosed infection is essential. The DICI-VIH study, a cluster-randomized crossover trial conducted in eight French emergency departments (EDs), found that a strategy combining nurse-driven targeted HIV screening with routine diagnostic testing was effective. AIM: The aim was to investigate factors associated with the implementation of HIV screening targeting key populations in EDs. METHODS: A self-administered questionnaire was distributed at registration to patients aged 18-64 years and able to give consent during the DICI-VIH intervention. Based on their responses, those belonging to key populations were offered a rapid test by triage nurses. Two key stages of the process were evaluated: questionnaire distribution by providers and test acceptance by patients. Patient information, daily workload, and ED characteristics were collected. The associations between these variables and (a) the proportion of questionnaires distributed and (b) the proportion of tests accepted were evaluated using multilevel modeling in order to examine differences in screening implementation between EDs. RESULTS: Questionnaire distribution proportions varied from 23% to 48% across EDs. They were higher on weekdays than weekends (odds ratio, OR: 3.77; 95% CI: 3.57-3.99) and when research staff participated (OR: 1.31; 95% CI: 1.26-1.37). They decreased over time (OR: 0.76; 95% CI: 0.71-0.82; 4th [Q3] vs. 1st quartile [Q0] of intervention days) and with increased patient flow (OR: 0.61; 95% CI: 0.56-0.67; Q3 vs. Q0 of eligible patients). Test acceptance varied from 64% to 77% across EDs, increased with research staff participation (OR 1.20; 95% CI: 1.03-1.40), and decreased over time (OR: 0.75; 95% CI: 0.60-0.92; Q3 vs. Q0). Patients who accepted were more likely to be younger (OR: 0.76; 95% CI: 0.61-0.96; 50-64-year-old vs. 30-39-year-old patients). LINKING EVIDENCE TO ACTION: Patient flow, intervention duration, weekdays, and research staff participation were important determinants of targeted screening implementation. These findings could help guide future implementation in similar settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".