Words that describe chronic musculoskeletal pain: implications for assessing pain quality across cultures
Bibliographic record
Abstract
Saurab Sharma,1 Anupa Pathak,2 Mark P Jensen3 1Department of Physiotherapy, Kathmandu University School of Medical Sciences, Dhulikhel Hospital Kathmandu University Hospital, Dhulikhel, 2Department of Physiotherapy, Kathmandu University School of Medical Sciences, Dhulikhel, Kavre, Nepal; 3Department of Rehabilitation Medicine, University of Washington, Seattle, WA, USA Background: People from different cultures who speak different languages may experience pain differently. This possible variability has important implications for evaluating the validity of pain quality measures that are directly translated into different languages without cultural adaptations. The aim of this study was to evaluate the impact of language and culture on the validity of pain quality measures by comparing the words that individuals with chronic pain from Nepal use to describe their pain with those used by patients from the USA. Methods: A total of 101 individuals with chronic musculoskeletal pain in Nepal were asked to describe their pain. The rates of the different pain descriptor domains and phrases used by the Nepali sample were then compared to the published rates of descriptors used by patients from the USA. The content validity of commonly used measures for assessing pain quality was then evaluated. Results: While there was some similarity between patients from Nepal and the USA in how they describe pain, there were also important differences, especially in how pain quality was described. For example, many patients from Nepal used metaphors to describe their pain. Also, the patients from Nepal often used a category of pain descriptor – which describes a physical state – not used by patients from the USA. Only the original McGill Pain Questionnaire was found to have content validity for assessing pain quality in patients from Nepal, although other existing pain quality measures could be adapted to be content valid by adding one or two additional descriptors, depending on the measure in question. Conclusion: The findings indicate that direct translations of measures that are developed using samples of patients from one country or culture are not necessarily content valid for use in other countries or cultures; some adaptations may be required in order for such measures to be most useful in new language and culture. Keywords: pain quality, pain assessment, chronic pain, cross-cultural adaptation, musculoskeletal pain
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".