The Ecology of Engagement: Fostering cooperative efforts in health with patients and communities
Bibliographic record
Abstract
CONTEXT: Patients and community members are engaged in nearly every aspect of health systems. However, the engagement literature remains siloed and fragmented, which makes it difficult to connect engagement efforts with broader goals of health, equity and sustainability. Integrated and inclusive models of engagement are needed to support further transformative efforts. METHODS: This article describes the Ecology of Engagement, an integrated model of engagement. The model posits that: (1) Health ecosystems include all members of society engaged in health; (2) Engagement is the 'together' piece of health and healthcare (e.g., caring for each other, preventing, researching, teaching and building policies together); (3) Health ecosystems and engagement are interdependent from each other, both influencing health, equity, resilience and sustainability. CONCLUSION: The Ecology of Engagement offers a common sketch to foster dialogue on engagement across health ecosystems. The model can drive cooperative efforts with patients and communities on health, equity, resilience and sustainability. PATIENTS AND PUBLIC CONTRIBUTION: Three of the authors have lived experiences as patients. One has a socially disclosed identity as a patient partner leader with extensive experience in engagement (individual care, education, research, management and policy). Two authors have significant experience as patients and informal caregivers, which were mobilized in descriptive illustrations. A fourth author has experience as an engaged citizen in health policy debates. All authors have professional lived experience in health (manager, researcher, health professional, consultant and educator). Six patient and caregiver partners with lived experience of engagement (other than the authors) contributed important revisions and intellectual content.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.045 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.003 | 0.055 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".