Cognitive Testing of PAINReportIt in Adult African Americans With Sickle Cell Disease
Bibliographic record
Abstract
PAINReportIt, a computerized version of the McGill Pain Questionnaire (Pain. 1975, 1:277-299), presents pain measurement items to responders in serial display screens accompanied by pop-up screens. In this study, we used cognitive interviews to examine further validity of PAINReportIt with 25 African Americans with sickle cell disease. The specific aims were to determine if the questions in the PAINReportIt program were relevant to and understood by African Americans with sickle cell disease and to describe the nature of the pain they experienced. Most study participants were enthusiastic and able to use the tool as intended and appreciated the comprehensiveness, detail, and multidimensionality of its pain data. For some screens, two to six participants' responses suggested some question understanding and interpretation issues, inability to retrieve the requested information, or technical issues. Their responses indicated that screens lacked sufficient specificity for the temporal nature of pain recurrence over a lifetime. The program captured both nociceptive and neuropathic aspects of sickle cell pain and provided detailed information on the location, intensity, quality, and pattern of pain experienced by participants. We recommend that future revisions to the PAINReportIt program address the temporal issues of measuring recurrent pain, resolve technological issues related to pop-ups, and simplify difficult words to better match the typical health literacy levels of patients. These revisions could further enhance the technological aspects, usability, and cultural appropriateness of the tool for African Americans with sickle cell disease.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".