The ultimate power play in research - partnering with patients, partnering with power
Bibliographic record
Abstract
BACKGROUND: Patient and public involvement (PPI), also called patient engagement, patient partnership, or consumer involvement, holds potential to change approaches and outcomes in research and healthcare. All research teams have complex power dynamics, including those with patient/public members. We present our perceptions and understandings of power arising from our own experiences on health research teams. We suggest ways for members of health research teams to move forward in efforts to minimize power discrepancies. MAIN BODY: As an international group of patients, caregivers, and research allies, we have experienced power dynamics within PPI collaborations and believe they must be challenged to achieve more equitable partnerships. We explore four themes relating to power in no order of importance: (1) The unstable and changing nature of power in PPI. Patient/public partners' abilities and capacities to engage equally depend on the working environment and on their economic, cultural, social and symbolic (including health) capitals; (2) Power between and amongst patients/public partners. Layers of power exist between and amongst patient/public partners and their networks, which may lead to a lack of diversity in partners and/or bullying and requires recognizing that not all patient/public partners bring the same experiences, skills or perspectives to research teams; (3) Power and tokenism. Tokenism occurs when patient/public perspectives in PPI are mostly ignored, results when power and resources are disproportionately concentrated, and can be perpetuated by funding and funding agency infrastructures; and, (4) PPI as a commodity or product. PPI may be seen or used as a means to extract experiences or validate one's work without truly involving patients/public contributors in the research design and process. PPI aligns with a broader trend of academic research methodologies grounded in standpoint epistemology (that is, how a person's social identity influences what they know). We include practical recommendations for researchers and for patient/public partners to share power more equitably on research teams. CONCLUSION: In our experiences on health research teams, patient/public partners are often the most vulnerable and most disadvantaged members of the team who experience the largest power inequities. We hope our identified themes about power, the context related to power, and our reflections and recommendations on them inspire those holding power on research teams to share that power.
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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 | 0.056 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.064 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".