Gender differences in information needs and preferences regarding depression among individuals with multiple sclerosis, inflammatory bowel disease and rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVE: We assessed the information needs of persons with any of three immune-mediated inflammatory diseases (multiple sclerosis [MS], inflammatory bowel disease [IBD] and rheumatoid arthritis [RA]) regarding depression, as a first step toward developing patient-relevant information resources, ultimately to facilitate self-management and appropriate care. We also compared information needs across genders. METHODS: We surveyed participants with MS, IBD and RA regarding depression-related information needs including types of treatments, effectiveness, risks, benefits, and perceived helpfulness of treatments. We compared responses between groups using multivariate regression. RESULTS: 328 participants provided complete responses (MS: 141, IBD: 114, RA: 73). Most of the topics queried were perceived as very important, and similarly important for all groups. Women placed higher importance than men on most topics. The most popular formats for receiving information were discussion with a counselor (very preferred: 67.4%) and written information (very preferred: 65.5%); this did not differ between groups. CONCLUSIONS: Persons affected by MS, IBD and RA are interested in receiving information about multiple topics related to depression treatment, from multiple sources. Women desire more information than men. PRACTICE IMPLICATIONS: These findings can be used to design information resources to meet information needs regarding depression in MS, IBD and RA.
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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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".