Health Care Disparities, Social Determinants of Health, and Emotional Impacts in Patients with Ulcerative Colitis: Results from a Global Ulcerative Colitis Narrative Patient Survey
Bibliographic record
Abstract
BACKGROUND: The Ulcerative Colitis (UC) Narrative global survey assessed aspects of living with UC. This analysis aimed to identify health care disparities, social determinants of health, and emotional impacts related to UC disease management, patient experience, and quality of life. METHODS: The survey was conducted by The Harris Poll from August 2017 to February 2018 among adults with UC. Responses from 1000 patients in the United States, Canada, Japan, France, and Finland were analyzed based on patient income, employment status, educational level, age, sex, and psychological comorbidities. Odds ratios (ORs) with significant P values (P < .05) from multivariate logistic regression models are reported. RESULTS: Low-income vs high-income patients were less likely to have participated in a peer mentoring (OR, 0.30) or UC education program (OR, 0.51). Patients not employed were less likely to report being in "good/excellent" health (OR, 0.58) than patients employed full time. Patients with low vs high educational levels were less likely to have reached out to patient associations/organizations (OR, 0.59). Patients aged younger than 50 years vs those aged 50 years and older were less likely to have visited an office within an inflammatory bowel disease center/clinic in the past 12 months (OR, 0.53). Males were less likely to be currently seeing their gastroenterologist than females (OR, 0.66). Patients with vs without depression were less likely to agree that UC had made them more resilient (OR, 0.51). CONCLUSIONS: Substantial differences in disease management and health care experience were identified, based on categories pertaining to patient demographics and psychological comorbidities, which may help health care providers better understand and advance health equity to improve patient care.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".