How can we improve models of care in inflammatory bowel disease? An international survey of IBD health professionals
Bibliographic record
Abstract
BACKGROUND AND AIMS: Few studies have specifically examined models of care in IBD. This survey was designed to help gather information from health professionals working in IBD services on current care models, and their views on how to best reshape existing models for IBD care worldwide. METHODS: An online mixed-methods survey was conducted with health professionals caring for IBD patients. Recruitment was conducted using the snowballing technique, where members of professional networks of the investigators were invited to participate. Results of the survey were summarised using descriptive statistics. RESULTS: Of the 135 included respondents, 76 (56%) were female, with a median age of 44 (range: 23-69) years, 50% were GI physicians, 34% nurses, 8% psychologists, 4% dieticians, 2% surgeons, 1% psychiatrists, and 1% physiotherapists. Overall, 73 (54%) respondents considered their IBD service to apply the integrated model of care, and only 5% reported that they worked exclusively using the biomedical care (no recognition of psychosocial factors). The majority of respondents reported including mental health assessment in their standard IBD care (65%), 51% believed that an ideal IBD service should be managed in specialist led clinics, and 64% wanted the service to be publicly funded. Respondents pictured an ideal IBD service as easy-access fully multi-disciplinary, with a significant role for IBD nurses and routine psychological and nutritional assessment and care. CONCLUSIONS: Health care professionals believe that an ideal IBD service should: be fully integrated, involve significant roles of nurses, psychologists and dieticians, run in specialist clinics, be easily accessible to patients and publicly funded.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".