Lessons learned about art-based approaches for disseminating knowledge
Bibliographic record
Abstract
AIM: To present a case example of using an arts-based approach and the development of an art exhibit to disseminate research findings from a narrative research study. BACKGROUND: Once a study has been completed, the final step of dissemination of findings is crucial. In this paper, we explore the benefits of bringing nursing research into public spaces using an arts-based approach. DATA SOURCES: Findings from a qualitative narrative study exploring experiences of living with life-threatening illnesses. REVIEW METHODS: Semi-structured in-depth interviews were conducted with 32 participants living with cancer, chronic renal disease, or HIV/AIDS. Participants were invited to share a symbol representing their experience of living with life-threatening illness and the meaning it held for them. DISCUSSION: The exhibit conveyed experiences of how people story and re-story their lives when living with chronic kidney disease, cancer or HIV. Photographic images of symbolic representations of study participants' experiences and poetic narratives from their stories were exhibited in a public art gallery. The theoretical underpinning of arts-based approaches and the lessons learned in creating an art exhibit from research findings are explored. CONCLUSION: Creative art forms for research and disseminating knowledge offer new ways of understanding and knowing that are under-used in nursing. IMPLICATIONS FOR PRACTICE/RESEARCH: Arts-based approaches make visible patients' experiences that are often left unarticulated or hidden. Creative dissemination approaches such as art exhibits can promote insight and new ways of knowing that communicate nursing research to both public and professional audiences.
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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 | 0.073 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".