Growing a Healthy Ecosystem for Patient and Citizen Partnerships
Bibliographic record
Abstract
Patient and citizen engagement is taking root in a number of healthcare organizations.These initiatives show promising results but require a supportive environment to bring systemic and sustainable impacts.In this synthesis article, we propose an ecosystemic perspective on engagement in health, outlining key elements at the individual, organizational and systemic levels supporting reciprocal and effective relationships among all partners to provide conditions for the co-production of health and care.We argue that growing a healthy engagement ecosystem requires: (1) building local and national "hubs" to facilitate learning and capacity building across engagement domains, populations and contexts; (2) supporting reciprocal partnerships based on co-leadership; and (3) strengthening capacities for research, evaluation and co-training of all partners to support reflective engagement practices that bring about effective change. An Ecosystemic, Reciprocal Perspective on Patient and Citizen Engagement RelationshipsEcosystems are communities of individuals interacting with their environment (Gurevitch et al. 2002: 522).Ecosystems are "holonic structures": they are made of entities that are a whole and a part of a larger system at the same time (e.g., atoms, cells, organisms, planet), with the levels dynamically interacting with one another (Koestler 1967: 48).In healthcare, individuals are embedded within the healthcare organizations and systems they interact with (Mella and Gazzola 2017).An ecosystemic perspective on patient and citizen engagement reminds us that healthcare, in its essence, is about relationships between people.This perspective also highlights the idea that these relationships
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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 | 0.033 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.023 |
| Scholarly communication | 0.029 | 0.035 |
| Open science | 0.003 | 0.056 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".