Toward an Ecosystemic Approach to Chronic Care Design and Practice in Primary Care
Bibliographic record
Abstract
Despite the increasing prevalence of chronic conditions and multimorbidities, the essential attributes of the structure and delivery of primary care continue to be defined in terms of disease-specific approaches and acute conditions. Effective improvements will require alternative ways of thinking about chronic care design and practice. This essay argues for an ecosystemic understanding of chronic care founded on a communal and a dynamic view of the response of the patient, family, and health professionals to chronic illness. The communal view highlights the cocreative nature of the response to illness and the need to integrate the skills and resources of all the participants; what and how the participants learn in the course of the illness become central to chronic care. The dynamic view draws attention to the unfolding of illness management activities over time and to the need to engage the illness at specific time points or recurring time intervals that have the potential for important change in the experience of the participants. Chronic care would then include design for community, with an emphasis on the patient and family as necessary participants in the health care team. It would also include design for emergent learning and practice whereby health professionals go beyond standardization of care processes to develop new ways to harness the participants' imagination and learn from the changing experience of illness. Health professionals would also learn to cultivate trust, communal engagement, and openness to experimentation that facilitate collective learning, and help sharpen the participants' responsiveness to the emergent.
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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 | 0.029 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.072 |
| Scholarly communication | 0.025 | 0.016 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.007 | 0.006 |
| 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".