Piloting a Virtual Arts-Based Methodology to Explore Children’s Experiences of Chronic Pain: Methodological Insights and Lessons Learned
Bibliographic record
Abstract
In the field of childhood pain, the knowledge and expertise of children has long been overlooked. Instead, adult knowledge has been privileged over child knowledge, despite contemporary understandings that the experience of pain is subjective in nature and can only be fully appreciated by the person experiencing it – regardless of age, stage, or status. In this paper, we report on a pilot study that combined virtual semi-structured interviewing methods with arts-based research methods (drawing or painting, produced offline in the time between virtual interviews) to explore children’s experiences of chronic pain from their own perspectives. We use this study as a backdrop to make visible the ‘behind the scenes’ methodological work of arts-based research with children, paying particular attention to the ways in which our methodological approach created time and space for reflection, supported the co-production of knowledge, provided a means through which to visualize the effect of broader social influences on knowledge production, and provoked novel lines of analysis and inquiry. All these affordances call for deeply reflexive research practices. We suggest the methodological approach described in this paper can help amplify and add value to research with young children. The richness of the children’s accounts concerning chronic pain add to the body of evidence demonstrating that ‘even’ young children have knowledge, expertise, and insights that should be elicited to expand understandings of children’s pain and other similarly abstract topics, phenomenon, and lines of inquiry in health.
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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 | 0.059 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".