Living with hard-to-heal wounds: A global health challenge explored through analytic sketches
Bibliographic record
Abstract
Chronic, hard-to-heal wounds are a major public health concern, putting a strain on healthcare systems and causing significant psychosocial distress for patients. This article details a project that engaged 11 people with chronic wounds and their care partners as subject matter experts. We used a visual method called analytic sketching to analyze their experiences. Analytic sketches are a cognitive tool that helps researchers identify patterns, gain deeper insights, and uncover experiences that might not be obvious from text alone. This approach enhances the trustworthiness and rigor of the findings, especially when combined with verbal data and techniques like participant checking. The sketches visually revealed several key psychosocial themes. Color was used to depict the pain experienced by patients and their caregivers, the importance of support, and a sense of chaos. Ink lines and strokes highlighted the frustration and communication failures with healthcare professionals and the barriers to care. Finally, visual representations were presented to express a collective experience of navigating the complex political realities of wound care within a publicly funded system. By showcasing six representative sketches, this article demonstrates the effectiveness of using arts-based methods to analyze complex healthcare experiences. The findings have important implications for both clinical practice and policy development, offering a more holistic understanding of living with chronic wounds.
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| 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".