Pain assessment in native and non-native language: difficulties in reporting the affective dimensions of pain
Bibliographic record
Abstract
Background and aims The language in assessing intensity or quality of pain has been studied but the results have been inconsistent. The physicians' language skills might affect the estimation of the severity of pain possibly leading to insufficient use of analgesics. Several interfering cultural factors have complicated studies aimed at exploring the language used to detect the quality of pain. We aimed to compare native and non-native language related qualitative aspects of pain chosen by Swedish speaking patients with diabetes. Methods In the study participated 10 Finnish and 51 Swedish speaking patients with diabetes. The Pain Detect-questionnaire was used for clarifying the patients' pain and the mechanism of their pain (neuropathic or not) and for assessing the intensity and quality of pain. In addition, the patients completed the short-form McGill Pain Questionnaire (sfMPQ) in Finnish (test I). After 30 min the subjects completed the sfMPQ a second time in their native language (test II). The Swedish speakers estimated their second language, Finnish, proficiency on a 5-graded scale. Results There were significantly more discrepancies between sfMPQ test I and test II among the Swedish speaking respondents who reported poor (hardly none) Finnish language proficiency compared with those with good Finnish proficiency. Discrepancies occurred especially between the affective qualities of pain. Conclusions Poor second language proficiency exposes Swedish speakers to pain communication difficulties related to the affective aspects of pain. Consequently, discordant language communication could cause underestimation of the severity of pain and pain undertreatment. Implications To ensure adequate pain treatment measuring the affective dimension of pain in the patient's native language is crucial.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".