Illuminating the Impact of a Participatory Art Exhibition for Knowledge Translation in Oncology: A Case Study (Lumière sur les retombées d’une exposition d’art participative pour le transfert des connaissances en oncologie : une étude de cas)
Bibliographic record
Abstract
There is a notable research-practice gap in health care, alongside growing interest in arts-based knowledge translation to honor participants’ voices, advance equitable access, and enact change. We created an art exhibition to share findings about patients’ experiences of a mindfulness-based expressive arts group in oncology. Guided by the Knowledge-as-Action Framework and using a case study design, our research question was: How does an exhibition displaying artwork made by patients with cancer facilitate the communication of research findings with diverse audiences? Questionnaires, field notes, documents, and photographs were analyzed using descriptive statistics and content analysis. The development of a portable, in-person art exhibition required an intensive interdisciplinary, research-practice collaboration. The exhibition was held at a regional cancer center, a congress, and two Canadian universities. Over 600 people attended the exhibition, including patients, the public, healthcare professionals, policy-makers, students, and academic faculty/staff. We recruited 190 research participants and found the exhibition was an immersive experience that deepened understandings of the patient-artists’ experiences and catalyzed intentions for practice change. Researchers and clinicians should consider arts-based knowledge translation strategies that honor and amplify patients’ voices, making their experiences more accessible and memorable for diverse stakeholders, while fostering empathy and an appreciation of arts interventions.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".