What works in implementation of integrated care programs for older adults with complex needs? A realist review
Bibliographic record
Abstract
PURPOSE: A realist review of the evaluative evidence was conducted on integrated care (IC) programs for older adults to identify key processes that lead to the success or failure of these programs in achieving outcomes such as reduced healthcare utilization, improved patient health, and improved patient and caregiver experience. DATA SOURCES: International academic literature was searched in 12 indexed, electronic databases and gray literature through internet searches, to identify evaluative studies. STUDY SELECTION: Inclusion criteria included evaluative literature on integrated, long-stay health and social care programs, published between January 1980 and July 2015, in English. DATA EXTRACTION: Data were extracted on the study purpose, period, setting, design, population, sample size, outcomes, and study results, as well as explanations of mechanisms and contextual factors influencing outcomes. RESULTS OF DATA SYNTHESIS: A total of 65 articles, representing 28 IC programs, were included in the review. Two context-mechanism-outcome configurations (CMOcs) were identified: (i) trusting multidisciplinary team relationships and (ii) provider commitment to and understanding of the model. Contextual factors such as strong leadership that sets clear goals and establishes an organizational culture in support of the program, along with joint governance structures, supported team collaboration and subsequent successful implementation. Furthermore, time to build an infrastructure to implement and flexibility in implementation, emerged as key processes instrumental to success of these programs. CONCLUSIONS: This review included a wide range of international evidence, and identified key processes for successful implementation of IC programs that should be considered by program planners, leaders and evaluators.
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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 | 0.038 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".