An Environmental Scan and Appraisal of Patient Online Resources for Managing Rheumatoid Arthritis Flares
Bibliographic record
Abstract
OBJECTIVE: To conduct an environmental scan and appraisal of online patient resources to support rheumatoid arthritis (RA) flare self-management. METHODS: We used the Google search engine (last search March 2023) using the terms "rheumatoid arthritis" and "flare management." Additional searches targeted major arthritis organizations, as well as regional, national, and international resources. Appraisal of the resources was conducted by 2 research team members and 1 patient partner to assess the understandability and actionability of the resource using the Patient Education Materials Assessment Tool (PEMAT). Resources rating ≥ 60% in both domains by either the research team or the patient partner were further considered for content review. During content review, resources were excluded if they contained product advertisements, inaccurate information, or use of noninclusive language. If content review criteria were met, resources were designated as "highly recommended" if both patient partners and researchers' PEMAT ratings were ≥ 60%. If PEMAT ratings were divergent and had a rating ≥ 60% from only 1 group of reviewers, the resource was designated "acceptable." RESULTS: We identified 44 resources; 12 were excluded as they did not pass the PEMAT assessment. Fourteen resources received ratings ≥ 60% on understandability and actionability from both researchers and patient partners; 10 of these were retained following content review as "highly recommended" flare resources. Of the 18 divergent PEMAT ratings, 8 resources were retained as "acceptable" following content review. CONCLUSION: There is high variability in the actionability and understandability of online RA flare materials; only 23% of resources were highly recommended by researchers and patient partners.
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 | 0.036 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.022 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".