Elicit an initial program theory on the scale-up of integrated care programs: first step of a realist synthesis
Bibliographic record
Abstract
Background: Realist synthesis is a comprehensive method of review for evaluating complex programs such as integrated models of care. The first step of the synthesis is to clarify the scope of the review and to explore existing theories in the literature related to targeted programs. The aim is to elicit an initial program theory in the form of context (C), mechanism (M) and outcome (O) configurations, which serve as a reference model for data collection and analysis from studies included in the synthesis. Objective: To develop an initial program theory about scale-up of integrated care programs. Study Design: This first step of our realist synthesis was conducted using a participatory approach with stakeholders (patient partners, clinicians, decision-makers, and academic researchers). This step requires active and ongoing dialogue with the people who develop, deliver, or use the program. Working sessions, as well as training on the realist approach, were organized with stakeholders to identify the research question, refine the purpose of the review, and articulate key theories to be explored. Program: This synthesis is part of a realist evaluation of a case management intervention in primary healthcare for people with complex needs in three Canadian provinces as part of the PriCARE Integration research program. Results: Thirteen theories were identified based on a preliminary exploration of the literature on implementation research and scale up of integrated care programs. The Normalization Process Theory (Murray et al., 2018), the ExpandNet/WHO framework for scaling up (WHO and ExpandNet, 2009), and the theory of community-integrated care approaches (Mukumbang et al., 2022) were selected based on their relevance for describing the actors, mechanisms, contexts, and outcomes of scale-up of integrated care programs. These theories were presented to stakeholders during a working session for feedback and an initial program theory was developed with CMO configurations. This theory will be exhibited in detail during the presentation. Learnings: The presentation will provide guidance to international academic researchers, patient partners and healthcare providers on how to engage stakeholders in a realist synthesis, especially during preliminary steps; it could also be useful for stakeholders interested in the scale up of integrated care programs to facilitate reasoning about the contextual elements and the mechanisms they need to consider for scaling up. Next steps: The second step of the realist synthesis will be a literature search conducted to identify relevant material than can contribute to the initial program theory and make recommendations for the scale up of the case management program. A realist evaluation will then be conducted to test and iteratively refine the program theory.
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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 | 0.217 | 0.274 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".