Using the Power Wheel as a transformative tool to promote equity through spaces and places of patient engagement
Bibliographic record
Abstract
BACKGROUND: Patient engagement is the active collaboration between patient partners and health system partners towards a goal of making decisions that centre patient needs-thus improving experiences of care, and overall effectiveness of health services in alignment with the Quintuple Aim. An important but challenging aspect of patient engagement is including diverse perspectives particularly those experiencing health inequities. When such populations are excluded from decision-making in health policy, practice and research, we risk creating a healthcare ecosystem that reinforces structural marginalisation and perpetuates health inequities. APPROACH: Despite the growing body of literature on knowledge coproduction, few have addressed the role of power relations in patient engagement and offered actionable steps for engaging diverse patients in an inclusive way with a goal of improving health equity. To fill this knowledge gap, we draw on theoretical concepts of power, our own experience codesigning a novel model of patient engagement that is equity promoting, Equity Mobilizing Partnerships in Community, and extensive experience as patient partners engaged across the healthcare ecosystem. We introduce readers to a new conceptual tool, the Power Wheel, that can be used to analyse the interspersion of power in the places and spaces of patient engagement. CONCLUSION: As a tool for ongoing praxis (reflection +action), the Power Wheel can be used to report, reflect and resolve power asymmetries in patient-partnered projects, thereby increasing transparency and illuminating opportunities for equitable transformation and social inclusion so that health services can meet the needs and priorities of all people.
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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 | 0.037 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.015 | 0.099 |
| Scholarly communication | 0.024 | 0.035 |
| Open science | 0.004 | 0.034 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".