Bibliographic record
Abstract
For over two decades, health services researchers and managers have focused on improving integration between organizations and levels of care. Yet, healthcare systems capable of delivering integrated care have not developed widely. This dissertation argues that structural and process strategies for integration need to be supplemented by attention to the social cognitions that characterize the behaviours of actors within healthcare systems. The aim of this dissertation is to explore how Shared Mental Model Theory might advance our understanding and measurement of integration processes and performance. The first paper in this dissertation examines the evolution of healthcare integration strategies over twenty-five years as reported in the academic literature. Six major, inter-related shifts were identified in strategy content. This evolution in conceptualization and practice highlights the importance of attention to meanings and perceptions. The second paper draws from Shared Mental Model Theory, and an exploratory, theory-driven literature review, to identify mental model content specific to system-level integration efforts, and to develop a theoretical framework of the antecedents, moderators and outcomes of shared mental models of integration. The final paper validates and improves the proposed content framework using a two-round, web-based modified Delphi process with a diverse, pan-Canadian group of integration experts, including policymakers, planners, managers, care providers, educators, researchers and patient advocates. The proposed “Integration Mindsets Framework” may be used to facilitate the planning, implementation, management and evaluation of integration initiatives. A shared mental models lens complements and fills the gaps of current approaches to the study of integration in the healthcare sector by focusing attention on the evolution and interplay of meanings, interpretations and knowledge about integration – and their potential impact on practice. Together, these studies provide theory-based, expert-validated constructs and frameworks that enable researchers and practitioners to describe, conceptualize and analyze health systems integration from a socio-cognitive perspective.
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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 | 0.044 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.024 | 0.040 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.006 | 0.011 |
| 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".