Bibliographic record
Abstract
BACKGROUND: Language is an important tool in the communication of pain. The most widely known verbal pain assessment tool is the McGill Pain Questionnaire (MPQ). The MPQ scoring procedure implies equal weights for different pain describing adjectives with the same rank value. AIM: In the present study the relative intensity of the most severe sensory pain adjectives of the MPQ was examined by means of Thurstone's method of paired comparison. METHOD: A questionnaire was constructed to run a balanced pairwise comparison experiment with pain describing adjectives obtained from the Dutch version of the MPQ. The questionnaire was filled out by psychology freshmen (N=528). RESULTS: Subjects were highly consistent in their choices regarding the paired comparisons but showed substantial individual differences with respect to agreement about the relative intensity of the adjectives. However, some adjectives were clearly rated as more painful than others, these were lancinating, bursting and tearing. Adjectives seen as relatively least painful were clasping, shooting and freezing. CONCLUSIONS: The results suggest that the MPQ scoring procedure is not entirely consistent with the relative ranking obtained in the present study. The authors do not consider the extent of this deviation to be a violation of one of the MPQ's underlying assumptions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".