Models of Care for Inflammatory Bowel Disease: A National Cross-sectional Survey to Characterize the Landscape of Inflammatory Bowel Disease Care in Canada
Bibliographic record
Abstract
Abstract Background Collaborative care models improve inflammatory bowel disease (IBD) patient outcomes, yet little is known about the capacity or available resources to deliver such model of care in Canada. We aimed to describe the structure and process characteristics of clinical care delivery models for IBD across Canada, including the number of collaborative care centers. Methods A cross-sectional study was conducted between November 2017 and October 2018 through an online survey. This survey was distributed to gastroenterologists at community and academic centers across Canada who provide care for IBD patients. Comparisons between collaborative and non-collaborative centers were analyzed using chi-squares or t-tests. Descriptive statistics of respondent demographics were also generated. Results Seventy-two gastroenterologists from 62 unique IBD centers completed the survey. A total of 7 unique collaborative centers and 55 unique non-collaborative centers were identified. There were significant differences between collaborative and non-collaborative centers in some aspects of access to IBD care, patient assessment and referral process, and patent education and empowerment. Notably, very few centers had processes for implementing and evaluating evidence-based clinical pathways, and auditing quality indicators. Conclusions Our findings identify areas for improving the quality of IBD care in Canada. Expanding the number of and access to collaborative care centers in Canada is needed, in addition to increased focus on patient education, communication, and implementation of evidence-based care pathways.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".