A type II implementation-effectiveness hybrid quasi-experimental pilot study of a clinical intervention to re-engage people living with HIV into care, ‘Lost & Found’: an implementation science protocol
Bibliographic record
Abstract
BACKGROUND: At the McGill University Health Centre (MUHC), 10% of patients living with HIV do not return for care annually. Currently, no formal system exists to re-engage out-of-care (OOC) patients. Lost & Found, developed using an implementation science approach, is an intervention to re-engage OOC patients. It is based on existing evidence-based interventions and will be adapted for use by nurses at the MUHC. The aims of this study are to simultaneously assess both implementation and effectiveness of Lost & Found in order to determine the viability of a future multisite stepped-wedge cluster randomised trial. METHODS: Lost & Found consists of two core elements: identifying and contacting OOC patients. Based on formative work involving MUHC nurses, and the use of a combined implementation framework (enhanced Replicating Effective Programs, Tailored Implementation for Chronic Diseases, and Proctor et al.'s implementation outcomes), we will adapt the intervention to our clinic. Adaptations include the creation of an OOC risk prediction tool, an automated real-time OOC list, and prioritization of high-risk OOC patients for re-engagement. Delivery and ongoing adaptation of the intervention will follow a three-pronged implementation strategy consisting of (1) promoting adaptability; (2) planning, engaging, executing, evaluating, and reflecting cycles; and (3) internal facilitation. This 15-month quasi-experimental pilot study adopts a type II implementation-effectiveness hybrid design. To evaluate implementation, a convergent parallel mixed-methods approach will guide the mixing of qualitative and quantitative data at time points throughout the study. In addition, descriptive and pre-post analyses, for each of the implementation and sustainability phases, will inform evaluations of the cumulative effectiveness and sustainability of the Lost & Found intervention. DISCUSSION: This study will provide preliminary evidence for (1) the utility of our chosen implementation strategies and (2) the effectiveness of the intervention. Ultimately, this information may be used to inform future re-engagement efforts using implementation science in other HIV care centres. In addition, the procedures and measurement tools developed for this study will be foundational to the development of a multi-site, randomised stepped wedge study that would provide more robust evidence in support of the Lost & Found intervention.
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".