Exploring Vietnamese Pain Terms and Pain Descriptors: To What Extent are the McGill Pain Questionnaire (MPQ) Words Employed in the Vietnamese Context?
Bibliographic record
Abstract
This study aims to investigate Vietnamese pain terms and pain descriptors with a focus on how the McGill Pain Questionnaire (MPQ) words are utilised by the Vietnamese patients. Semi-structured interviews were employed to collect data from twenty-six Vietnamese female cancer patients. The data were analysed using both quantitative and qualitative content analysis. The findings indicated that đau (hurt), nhức (ache), and đau-nhức (hurt and ache) are three basic pain terms in Vietnamese, with đau being a super-ordinate pain term. In addition, Vietnamese pain descriptors can be systematically classified into MPQ-VN descriptors and Non-MPQ-VN descriptors, with the latter being used far more frequently than the former. The study also found that MPQ descriptors could not reflect the patients’ pain experience comprehensively in the Vietnamese context although the Vietnamese employed the equivalents of MPQ descriptors of different categories. That the limitations of Melzack’s (1975) inventory of MPQ descriptors have been validated in Vietnamese has contributed to Vietnamese healthcare professionals’ understanding of how the patients communicate about their pain experience using language. The study has also shed lights on applied linguists’ research directions which can be extended to areas beyond language education, such as health, therapy, and counselling.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".