The Dimensions of Tokenism in Patient and Family Engagement: A Concept Analysis of the Literature
Bibliographic record
Abstract
Patient engagement (PE) has become embedded in discussions about health service planning and quality improvement, and the goal has been to find ways to observe the potential beneficial outcomes associated with PE. Patients and health care professionals use various terms to depict PE, for example, partnership and collaboration. Similarly, tokenism is consistently used to describe PE that has gone wrong. There is a lack of clarity, however, on the meanings and implications of tokenism on PE activities. The objective of this concept analysis was to examine the peer-reviewed and gray literature that has discussed tokenism to identify how we currently understand and use the concept. This review discusses 4 dimensions of tokenism: unequal power, limited impact, ulterior motives, and opposite of meaningful PE. These dimensions explicate the different components, meanings, and implications of tokenism in PE practice. The findings of this review emphasize how tokenism is primarily perceived as negative by supporters of PE, but this attribution depends on patients' preferences for engagement. In addition, this review compares the dimensions of tokenism with the levels of engagement in the International Association of the Public Participation spectrum. This review suggests that there are 2 gradations of tokenism; while tokenism represents unequal power relationships in favor of health care professionals, this may lead to either limited or no meaningful change or change that is primarily aligned with the personal and professional goals of clinicians, managers, and decision-makers.
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".