Creation and Initial Validation of a Picture-Based Version of the Limitations of Activity Domain of the SF-36
Bibliographic record
Abstract
Those with limited language comprehension or literacy face problems completing written questionnaires evaluating their health or physical status on which treatment plans are based. This brief report describes how a picture-based version of the 10 items in the limitations of activities section of the short form 36 health survey questionnaire (SF-36) was developed iteratively and then piloted. Study participants were 101 community-living volunteers (58 female and 43 male volunteers aged 18-93 yrs) educated to postsecondary level (52), high school grades 10-12 (44), and grade 9 or less (5). They first completed the picture-based SF-36 LoA and described verbally and in writing what they understood each picture to mean and then completed the English text version of the SF-36 limitations of physical activities domain for comparison assessment. Additional feedback suggested where pictures could be altered to increase information capture. Subjects rated their health as 26.7% excellent, 25.7% very good, 29.8% good, 10.9% fair, and 6.9% poor. Analysis showed strong correlation between text-based SF-36 LoA questions and the picture-based visual score-VSF-36 LoA-(intraclass correlation coefficient = 0.98) with question 10 correlating highest (intraclass correlation coefficient = 0.90) and question 2 lowest (intraclass correlation coefficient = 0.82). The VSF-36 LoA is the first picture-based version of the SF-36; good correlation with the text-based version and global need warrants further development to aid those with limited literacy or language comprehension.
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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".