Increased reengagement of out-of-care HIV patients using Lost & Found, a clinic-based intervention
Bibliographic record
Abstract
BACKGROUND: Negative health outcomes associated with being out of HIV care (OOC) warrant reengagement strategies. We aimed to assess effectiveness of Lost & Found, a clinic-based intervention to identify and reengage OOC patients. METHODS: Developed and delivered using implementation science, Lost & Found consists of two core elements: identification, operationalized through nurse validation of a real-time list of possible OOC patients; and contact, via nurse-led phone calls. It was implemented over a 12-month period (2018-2019) at the Chronic Viral Illness Service, McGill University Health Centre (CVIS-MUHC) during a type-II implementation-effectiveness hybrid pilot study. Descriptive outcomes of interest were identification as possibly OOC, OOC confirmation, contact, and successful reengagement. We present results from a pre-post analysis comparing overall reengagement to the year prior, using robust Poisson regression controlled for sex, age, and Canadian birth. Time to reengagement is reported using a Cox proportional hazards model. RESULTS: Over half (56%; 1312 of 2354) of CVIS-MUHC patients were identified as possibly OOC. Among these, 44% (n = 578) were followed elsewhere, 19% (n = 249) engaged in care, 3% (n = 33) deceased, 2% (n = 29) otherwise not followed, and 32% (n = 423) OOC. Of OOC patients contacted (85%; 359/423), 250 (70%) reengaged and 40 (11%) had upcoming appointments; the remainder were unreachable, declined care, or missed given appointments. Pre-post results indicate people who received Lost & Found were 1.18 [95% confidence interval (CI) 1.02-1.36] times more likely to reengage, and reengaged a median 55 days (95% CI 14-98) sooner. CONCLUSION: Lost & Found may be a viable clinic-based reengagement intervention for OOC patients. More robust evaluations are needed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".