Changing the model of HIV PrEP delivery – nurse-led telehealth in a metropolitan sexual health service: a retrospective analysis
Bibliographic record
Abstract
Background Innovative models in HIV pre-exposure prophylaxis (PrEP) delivery are required to reduce the burden on clinical services and provide convenience and access for clients. A nurse-led telehealth PrEP clinic ('TelePrEP') with free multi-modal testing pathway has been developed at Sydney Sexual Health Centre (SSHC). Methods Using a multi-model testing pathway, we reviewed retrospective electronic medical record of TelePrEP consultations at SSHC. Primary outcomes were demographic and behavioural characteristics, rates of attendance of TelePrEP appointments and follow-up screening, and rates of PrEP initiation, re-initiation and continuation. Secondary outcomes were length of time from screening to TelePrEP appointment, duration of TelePrEP appointments, adherence to guideline-indicated laboratory testing, and rates of HIV/STI identified through screening. We compared outcomes between the three screening pathways and by Medicare status. Results A total of 472 clients were reviewed. Majority were cis -gender male (99%), non-Medicare (77%), and overseas-born (86%). There was no significant difference in attendance rates between the three screening pathways. The majority of appointments referred through MyCheck (82%) resulted in PrEP continuation; 36% attended follow-up screening, with the highest rates of follow-up referred through a[TEST] (44%), and lowest through Xpress (22%). More non-Medicare clients (38%) attended follow-up screening than Medicare clients (27%). Adherence to national guidelines for testing was high, and screening identified two new HIV diagnoses. Conclusion Nurse-led TelePrEP model is feasible in overcoming issues of accessibility for key population groups including overseas-born MSM. We achieved high overall attendance rates, high adherence to guideline-indicated laboratory monitoring, and rapid linkage to treatment for clients with HIV identified on screening.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".