Do Gender-Specific and High-Resolution Three Dimensional Body Charts Facilitate the Communication of Pain for Women? A Quantitative and Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Chronic pain is more prevalent among women; however, the majority of standardized pain drawings are often collected using male-like androgynous body representations. OBJECTIVE: The purpose of this study was to assess whether gender-specific and high-resolution three-dimensional (3D) body charts facilitate the communication of pain for women. METHODS: Using mixed-methods and a cross-over design, female patients with chronic pain were asked to provide detailed drawings of their current pain on masculine and feminine two-dimensional (2D) body schemas (N=41, Part I) or on female 2D and 3D high-resolution body schemas (N=41, Part II) on a computer tablet. The consistency of the drawings between body charts were assessed by intraclass correlation coefficient (ICC) and Bland-Altman plots. Semistructured interviews and a preference questionnaire were then used to obtain qualitative and quantitative responses of the drawing experience. RESULTS: The consistency between body charts were high (Part I: ICC=0.980, Part II: ICC=0.994). The preference ratio for the masculine to feminine body schemas were 6:35 and 18:23 for the 2D to 3D female body charts. Patients reported that the 3D body chart enabled a more accurate expression of their pain due to the detailed contours of the musculature and bone structure, however, patients also reported the 3D body chart was too human and believed that skin-like appearance limited 'deep pain' expressions. CONCLUSIONS: Providing gender-specific body charts may facilitate the communication of pain and the level of detail (2D vs 3D body charts) should be used according to patients' needs.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".