Learning and Caring in Communities of Practice: Using Relationships and Collective Learning to Improve Primary Care for Patients with Multimorbidity
Bibliographic record
Abstract
We introduce a primary care practice model for caring for patients with multimorbidity. Primary care for these patients requires flexibility and ongoing coordination, and it often must be tailored to individual circumstances. Such complex and flexible care could be accomplished within communities of practice, whose participants are willing to learn from their shared practice, further each other's goals, share their stories of success and failure, and promote the continued evolution of collective learning. Primary care in these communities would be conceived as a complex adaptive process in which the participants use an iterative approach to care improvement that integrates what they learn and do collectively over time. Clinicians in these communities would define common goals, cocreate care plans, and engage in reflective case-based learning. As community members manage their knowledge, gain insights, and develop new care strategies, they can improve care for patients with multiple conditions. Using a mix of methods, future research should explore the conditions that are necessary for collective learning within communities of clinicians who care for patients with multimorbidity and who develop new knowledge in practice. By understanding these conditions, we can foster the development of collective learning and improve primary care for these patients.
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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 | 0.001 | 0.001 |
| 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".