Organizational Context and Capabilities for Integrating Care: A Framework for Improvement
Bibliographic record
Abstract
BACKGROUND: Interventions aimed at integrating care have become widespread in healthcare; however, there is significant variability in their success. Differences in organizational contexts and associated capabilities may be responsible for some of this variability. PURPOSE: This study develops and validates a conceptual framework of organizational capabilities for integrating care, identifies which of these capabilities may be most important, and explores the mechanisms by which they influence integrated care efforts. METHODS: The Context and Capabilities for Integrating Care (CCIC) Framework was developed through a literature review, and revised and validated through interviews with leaders and care providers engaged in integrated care networks in Ontario, Canada. Interviews involved open-ended questions and graphic elicitation. Quantitative content analysis was used to summarize the data. RESULTS: The CCIC Framework consists of eighteen organizational factors in three categories: Basic Structures, People and Values, and Key Processes. The three most important capabilities shaping the capacity of organizations to implement integrated care interventions include Leadership Approach, Clinician Engagement and Leadership, and Readiness for Change. The majority of hypothesized relationships among organizational capabilities involved Readiness for Change and Partnering, emphasizing the complexity, interrelatedness and importance of these two factors to integrated care efforts. CONCLUSIONS: Organizational leaders can use the framework to determine readiness to integrate care, develop targeted change management strategies, and select appropriate partners with overlapping or complementary profiles on key capabilities. Researchers may use the results to test and refine the proposed framework, with a focus on the hypothesized relationships among organizational capabilities and between organizational capabilities and performance outcomes.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".