Arts-Informed Research Dissemination in the Health Sciences: An Evaluation of Peoples’ Responses to “The 7,024th Patient” Art Installation
Bibliographic record
Abstract
Arts-informed dissemination of health care research is an emerging field of scholarship. Our team chose to use the arts as a means to disseminate findings from a study about patients’ experiences of open-heart surgery and recovery. We transformed patients’ stories, gathered through interviews and journal writings, into poetry and photographic imagery and displayed this within a 1,739 ft2 art installation titled “The 7,024th Patient.” Our intention was to use the arts as dissemination method that could convey the sentiments and perspectives of patients. To evaluate this novel method of dissemination in the health sciences, we conducted a study to analyze its effect on viewers. We used a narrative methodology with a multimodal theoretical lens. Thirty-four individuals participated in either an individual interview or a focus group. In addition, more than 200 anonymous, written comments were generated at research stations placed throughout the installation. In this article, we present the findings. Participants found this art installation of poetry and imagery to be a valid, meaningful, and authentic representation of patients’ experiences. They also described being immersed into patients’ journeys and evoking self-reflection. Based on this research, arts-informed dissemination is a powerful medium to report findings. Our work provides empirical evidence that expands the different ways to distribute research in the health and social sciences.
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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 | 0.085 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.004 | 0.004 |
| 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".