The use of Arts-Based Research in Chronic Pain: A Scoping Review
Bibliographic record
Abstract
Background: As an emerging approach, arts-based research holds potential to advance understanding of the complex and multidimensional experiences of chronic pain and means of communicating this experiential evidence. This scoping review aimed to map and explore the extent of arts-based research in chronic non-cancer pain, understand the rationale behind using arts-based research methods, and identify directions for future research. Methods: Databases PsycINFO, MEDLINE, EMBASE, and CINAHL were searched for eligible English-language articles from inception to November 2022. Out of 1321 article titles and abstracts screened for eligibility, 18 articles underwent full-text screening, with 14 ultimately meeting all inclusion criteria. We conducted a narrative synthesis of data extracted from the 14 reviewed articles. Results: The review articles focused on a wide range of chronic non-cancer pain conditions, with 12/14 employing qualitative methods (86%), one repeated measures experimental design, and another a multiphase, multimethod design. Seven articles described the use of drawing, painting, or mixed-media artwork; four used photography; two used body mapping; and one used e-book creation. The rationale for arts-based research included exploring and better understanding patients' experiences with chronic non-cancer pain, constructing an intervention, and investigating or validating a clinical tool. Nine articles reported that their arts-based research methods produced unintended therapeutic benefits for participants. Recommendations for future research included using arts-based research to better understand and communicate with patients and providers, exploring convergence with art therapy, and designing creative and flexible multiphased studies involving collaboration across disciplines. Conclusions: Despite the wide variation in sample and art modalities across reviewed articles, arts-based methods were considered suitable and highly effective for investigating chronic non-cancer pain.
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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 | 0.032 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.027 | 0.026 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".