Exploring the Role of Pictograms in the Comprehension of Pain
Bibliographic record
Abstract
Introduction: Pain is both difficult to see and to articulate and this is challenging for both patients and clinicians. The aim of this study was to develop and test pictograms to describe different pain qualities. Methods: 22 pictograms were developed for evaluation based on pain qualities of the short form McGill Pain Questionnaire, version 2 (SF-MPQ-2). An online matching survey was conducted and disseminated via social media in 2021. Results: An overall matching of 66% or higher between pictogram and pain qualities descriptors was considered a proper matching. This study was carried out internationally (males = 57, age=41y.o. ±16; females = 155, age=41y.o.±17) and in Poland (males=49, age =35y.o.±17; females = 164, age=35y.o.±16). There were 14 pictograms that did not achieve 66% matching in any country. 8 pictograms mutually in all subgroups achieved a matching score of ≥66% regardless of geographic location, sex, income, or education level. Discussion and Conclusions: These 8 pictograms can be used clinically once they have been redrawn to improve consistency, and future research in the design of pictograms representing pain qualities of the SF-MPQ-2 should focus on design improvements for the remaining 14 pain qualities with poor comprehensibility.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.111 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".