Achieving person-centred care through a team-based care ecosystem approach
Bibliographic record
Abstract
The implementation of Person-Centred Care (PCC) by primary care teams is complex. Framed through the Quadruple Aim, successful healthcare system redesigns result in improved health outcomes of individuals and populations, reduce costs, and ensure an engaged and productive workforce. However, how can primary care teams achieve the Quadruple Aim? This article provides a learning and performance framework to support PCC through a Team-Based Care (TBC) ecosystem approach. We developed our approach using action research to improve TBC orientations, workshops, and consultations for teams and their leaders in Urgent Primary Care Centres and Primary Care Networks in Canada. This paper provides a synthesis of our experience in the context of the relevant evidence. We aim to share our efforts and acknowledge that our experience is still ongoing and complemented by ongoing improvement activities by others in the TBC ecosystem.
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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 | 0.022 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".