Outcome mapping for health system integration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it