The Use of Arts‐Based Methods to Enhance Patient Engagement in Health Research
Bibliographic record
Abstract
INTRODUCTION: Patient and care partner engagement in research (PER) is important in generating knowledge to improve healthcare. Arts-based methods (ABM) use art in the research process to share aesthetic knowledge, which is knowledge that may be too complex to share only verbally. Together, PER and ABM are potentially synergistic, as both are participatory, problem-focused, dialogic, and collaborative; yet little is known of the utility of ABM for PER. METHODS: A narrative review was performed to identify, collate, and summarize the ways ABM has been used with PER and share the impacts of ABM on PER. The databases CINAHL, Scopus, and PubMed were searched, and 15 articles were included. RESULTS: A wide variety of ABM were used for PER, with some studies using multiple ABMs. The use of ABM for PER was reported to be decolonizing, shifted power from researchers to people with lived experience, and reduced tokenism. People with lived experience shared their knowledge directly through their art, deepening the understanding of their emotions, feelings, and relationships. CONCLUSION: Researchers should consider the benefits of the participatory nature of ABM and explore how to engage people with lived experience in their work beyond data collection. Researchers engaging people with lived experience should consider using ABM as a way to operationalize PER to elicit aesthetic knowledge and strengthen power equalization. PATIENT OR PUBLIC CONTRIBUTION: No patients or members of the public contributed to this review due to a lack of funding to support their meaningful involvement.
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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 | 0.111 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".