Electronic pain questionnaires: A randomized, crossover comparison with paper questionnaires for chronic pain assessment
Bibliographic record
Abstract
Electronic questionnaires for pain assessment are becoming increasingly popular. There have been no published reports to establish the equivalence or psychometric properties of common pain questionnaires administered via desktop computers. This study compared responses to paper (P) and touch screen electronic (E) versions of the Short-Form McGill Pain Questionnaire (SF-MPQ) and Pain Disability Index (PDI), while examining the role of computer anxiety and experience, and evaluating patient acceptance. In a randomized, crossover design 189 chronic pain patients completed P and E versions of the SF-MPQ and PDI, and self-ratings of anxiety, experience, relative ease and preference. Psychometric properties were highly similar for P and E questionnaires. For the SF-MPQ, 60% or more of subjects gave equivalent responses on individual descriptors and PPI scale, with 80% rating within +/-1 point for an 11-point VAS. Correlations for the SF-MPQ scales ranged from 0.68 to 0.84. For the PDI, 60% or more of subjects responded within +/-1 point on individual questions, and the total score correlation was 0.67. Comparison of mean difference scores revealed no significant differences between modes for any of the questionnaire items or scores. Anxiety and experience scores showed no significant associations through correlations and high/low comparisons. Although nearly half of subjects reported no computer training, anxiety ratings were low, and considerably more subjects rated the E questionnaires as easier and preferred. Findings are consistent with test-retest reliability data, and support the validity and acceptance of electronic versions of the SF-MPQ and PDI.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".