Implementation of Lost & Found, An Intervention to Reengage Patients Out of HIV Care: A Convergent Explanatory Sequential Mixed-Methods Analysis
Bibliographic record
Abstract
Being out of HIV care (OOC) is associated with increased morbidity and mortality. We assessed implementation of Lost & Found, a clinic-based intervention to reengage OOC patients. OOC patients were identified using a nurse-validated, real-time OOC list within the electronic medical records (EMR) system. Nurses called OOC patients. Implementation occurred at the McGill University Health Centre from April 2018 to 2019. Results from questionnaires to nurses showed elevated scores for implementation outcomes throughout, but with lower, more variable scores during pre-implementation to month 3 [e.g., adoption subscales (scale: 1-5): range from pre-implementation to month 3, 3.7-4.9; thereafter, 4.2-4.9]. Qualitative results from focus groups with nurses were consistent with observed quantitative trends. Barriers concerning the EMR and nursing staff shortages explained reductions in fidelity. Strategies for overcoming barriers to implementation were crucial in early months of implementation. Intervention compatibility, information systems support, as well as nurses' team processes, knowledge, and skills facilitated implementation.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".