Development of an environmental contextual factor item set relevant to global functioning and health in patients with axial spondyloarthritis
Bibliographic record
Abstract
OBJECTIVE: To describe the development of an Environmental contextual factors (EF) Item Set (EFIS) accompanying the disease specific Assessment of SpondyloArthritis international Society Health Index (ASAS HI). METHOD: First, a candidate item pool was developed by linking items from existing questionnaires to 13 EF previously selected for the International Classification of Functioning, Disability and Health (ICF) /ASAS Core Set. Second, using data from two international surveys, which contained the EF item pool as well as the items from the ASAS HI, the number of EF items was reduced based on the correlation between the item and the ASAS HI sum score combined with expert opinion. Third, the final English EFIS was translated into 15 languages and cross-culturally validated. RESULTS: The initial item pool contained 53 EF addressing four ICF EF chapters: products and technology (e1), support and relationship (e3), attitudes (e4) and health services (e5). Based on 1754 responses of axial spondyloarthritis patients in an international survey, 44 of 53 initial items were removed based on low correlations to the ASAS HI or redundancy combined with expert opinion. Nine items of the initial item pool (range correlation 0.21-0.49) form the final EFIS. The EFIS was translated into 15 languages and field tested in 24 countries. CONCLUSIONS: An EFIS is available complementing the ASAS HI and helps to interpret the ASAS HI results by gaining an understanding of the interaction between a health condition and contextual factors. The EFIS emphasizes the importance of support and relationships, as well as attitudes of the patient and health services in relation to self-reported health.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".