An end to the “muffin meeting”: Conceptualizing power and navigating tokenism in patient engagement for health leaders
Bibliographic record
Abstract
Patient engagement is emerging as a priority for Canadian health leaders. Alongside the proliferation of patient engagement efforts in healthcare organizations and networks, awareness that tokenism can potentially occur within such efforts, as well as strategies to mitigate it, are gaining increased attention. While many actions associated with more tokenistic forms of patient engagement have been identified, this article posits there is a need to pay critical attention to the concept and role of power in enabling these actions in the first place. Of particular importance is how power and knowledge work to shape healthcare organizations and can create unequal relations with the patients they seek to engage. Drawing on the literature, this article serves as a theoretical roadmap for health leaders to think critically about power, as well as a set of prompts that can be used to reflexively consider their role in navigating power dynamics in the context of patient engagement efforts. This article contends that building awareness of power is a critical step for health leaders and organizations and that navigating power differences is a necessary leadership competency for engaging patients in decision-making throughout all stages of healthcare improvement and organizational change efforts.
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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.028 | 0.073 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".