From Power Over to Power With: The PARTNER Tool for Transformation in Participatory Health Research
Bibliographic record
Abstract
Power is a pervasive and often underexamined force that shapes all aspects of participatory health research (PHR); however, translating theoretical understandings of power into practical strategies remains a challenge. This commentary critically examines the complexities of power in PHR, highlighting both its constraints and its potential as a transformative force in equity-driven research. The discussion is grounded in selective foundational theories of power, which provide the theoretical basis for understanding power in PHR. Building on these perspectives, expressions of power— Power Over , Power To , Power With , Power Within , and Power Through , serve as analytical lenses to identify and assess where power operates and how it can be navigated. To bridge the gap between theoretical discourse and actionable practice, the Power Assessment Reflexivity Tool for Navigating Equality in Research (PARTNER) is introduced as a heuristic. PARTNER, informed by lived and living experience co-researchers, is a tool designed to make the complexities of power more navigable, offering prompts to guide PHR teams in fostering equity and justice within their work. By recognizing power as both a barrier and a possibility, this commentary underscores the importance of moving beyond performative and abstract discussions of power. It offers a practical entry point for research teams committed to addressing power in ways that are impactful, substantive, and aligned with systemic change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.149 | 0.210 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.067 |
| Scholarly communication | 0.024 | 0.026 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.008 | 0.011 |
| 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".