The 2023 Impact of Inflammatory Bowel Disease in Canada: Access to and Models of Care
Bibliographic record
Abstract
Rising compounding prevalence of inflammatory bowel disease (IBD) (Kaplan GG, Windsor JW. The four epidemiological stages in the global evolution of inflammatory bowel disease. Nat Rev Gastroenterol Hepatol. 2021;18:56-66.) and pandemic-exacerbated health system resource limitations have resulted in significant variability in access to high-quality, evidence-based, person-centered specialty care for Canadians living with IBD. Individuals with IBD have identified long wait times, gaps in biopsychosocial care, treatment and travel expenses, and geographic and provider variation in IBD specialty care and knowledge as some of the key barriers to access. Care delivered within integrated models of care (IMC) has shown promise related to impact on disease-related outcomes and quality of life. However, access to these models is limited within the Canadian healthcare systems and much remains to be learned about the most appropriate IMC team composition and roles. Although eHealth technologies have been leveraged to overcome some access challenges since COVID-19, more research is needed to understand how best to integrate eHealth modalities (i.e., video or telephone visits) into routine IBD care. Many individuals with IBD are satisfied with these eHealth modalities. However, not all disease assessment and monitoring can be achieved through virtual modalities. The need for access to person-centered, objective disease monitoring strategies, inclusive of point of care intestinal ultrasound, is more pressing than ever given pandemic-exacerbated restrictions in access to endoscopy and cross-sectional imaging. Supporting learning healthcare systems for IBD and research relating to the strategic use of innovative and integrative implementation strategies for evidence-based IBD care interventions are greatly needed. Data derived from this research will be essential to appropriately allocating scarce resources aimed at improving person-centred access to cost-effective IBD 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".