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Record W4393318975 · doi:10.1177/08404704241239862

An end to the “muffin meeting”: Conceptualizing power and navigating tokenism in patient engagement for health leaders

2024· article· en· W4393318975 on OpenAlexaffabout
Steven Slowka

Bibliographic record

VenueHealthcare Management Forum · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsTokenismPower (physics)Health carePublic relationsContext (archaeology)Set (abstract data type)PsychologyPolitical scienceSociologyComputer science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.170
GPT teacher head0.469
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes2
Has abstractyes

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