Evaluation of integrated care: an updated rapid review
Bibliographic record
Abstract
Purpose This study updates and expands upon the seminal review by Strandberg-Larsen and Krasnik (2009) by examining evaluation methods for integrated healthcare delivery published between 2009 and 2022. Integrated care aims to enhance care coordination and patient-centeredness by bridging healthcare silos; however, evaluating such complex systems remains a challenge. Design/methodology/approach A rapid review methodology was employed, following Cochrane Rapid Review guidance. Four databases (EMBASE, MEDLINE/PubMed, Web of Science and Cochrane Library) were searched using a comprehensive set of integrated care-related terms. Inclusion criteria were based on structural, cultural and process aspects of integration, as well as methodological criteria such as theoretical grounding, data type and internal validity. A 10-point framework was used to classify identified studies. Findings Out of over 40,000 initial records, 11 studies met the inclusion criteria. Quantitative approaches were dominant, with only one study using a mixed-methods design. Tools such as the Practice Integration Profile and B3-Maturity Model addressed structural readiness, while others emphasized cultural elements like collaboration and accountability. Process-focused evaluations offered insights into coordination and stakeholder experiences. Findings reveal an over-reliance on quantitative tools and call for the integration of qualitative measures to better capture the dynamic, context-specific nature of integrated care. Originality/value This review provides updated, evidence-informed recommendations for evaluating integrated healthcare systems. It advocates for holistic, flexible and mixed-methods frameworks to support health leaders and policymakers in advancing integrated care.
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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 | 0.086 | 0.280 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.029 | 0.024 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".