Ulcerative Colitis Narrative Global Survey Findings: Communication Gaps and Agreements Between Patients and Physicians
Bibliographic record
Abstract
BACKGROUND: The Ulcerative Colitis (UC) Narrative global surveys examined patient and physician perspectives on living with UC and tried to identify gaps in optimal care. Questions explored patient-physician interactions, UC management goals, and resources for improving communication. METHODS: Questionnaires were conducted across 10 countries, covering aspects of UC including diagnosis, treatment, and impact on patient quality of life, in addition to standard demographic information. Descriptive statistics were calculated. RESULTS: Globally, 2100 patients and 1254 physicians were surveyed (from August 2017 to February 2018). Results showed 85% of patients were satisfied with the communication they had with their physician, including discussions relating to symptoms (86%) and medication options (81%). However, 72% of patients wished for more information and support at initial diagnosis, and 48% did not feel comfortable talking to their physician about emotional concerns. Most patients (71%) set UC management goals with their physician. Both patients (63%) and physicians (79%) wished for longer appointments. Although 84% of physicians believed patient advocacy organizations to be important in UC management, more than half (54%) never discussed them with patients. CONCLUSIONS: These survey results highlight overall patient satisfaction with patient-physician communication but emphasize areas for improvement, such as patient desire to have more information earlier in their disease course. There is an unmet need for better information, materials, and support. Physicians need to consider which of the available tools and resources can help patients talk more openly, and accurately, because informed patients are more likely to engage with physicians in a shared decision-making process.
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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 | 0.014 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".