Outcome mapping for health system integration
Bibliographic record
Abstract
Health systems around the world are implementing integrated care strategies to improve quality, reduce or maintain costs, and improve the patient experience. Yet few practical tools exist to aid leaders and managers in building the prerequisites to integrated care, namely a shared vision, clear roles and responsibilities, and a common understanding of how the vision will be realized. Outcome mapping may facilitate stakeholder alignment on the vision, roles, and processes of integrated care delivery via participative and focused dialogue among diverse stakeholders on desired outcomes and enabling actions. In this paper, we describe an outcome-mapping exercise we conducted at a Local Health Integration Network in Ontario, Canada, using consensus development conferences. Our preliminary findings suggest that outcome mapping may help stakeholders make sense of a complex system and foster collaborative capital, a resource that can support information sharing, trust, and coordinated change toward integration across organizational and professional boundaries. Drawing from the theoretical perspectives of complex adaptive systems and collaborative capital, we also outline recommendations for future outcome-mapping exercises. In particular, we emphasize the potential for outcome mapping to be used as a tool not only for identifying and linking strategic outcomes and actions, but also for studying the boundaries, gaps, and ties that characterize social networks across the continuum of 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 | 0.048 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".