Crohn’s and Colitis Canada’s 2021 Impact of COVID-19 and Inflammatory Bowel Disease in Canada: Health Care Delivery During the Pandemic and the Future Model of Inflammatory Bowel Disease Care
Bibliographic record
Abstract
The SARS-CoV-2 pandemic has had a profound impact on inflammatory bowel disease (IBD) health care delivery. The implementation of necessary public health restrictions has restricted access to medications, procedures and surgeries throughout the pandemic, catalyzing widespread change in how IBD care is delivered. Rapid large-scale implementation of virtual care modalities has been shown to be feasible and acceptable for the majority of individuals with IBD and health care providers. The SARS-CoV-2 pandemic has exacerbated pre-existing barriers to accessing high-quality, multidisciplinary IBD care that addresses health care needs holistically. Continued implementation and evaluation of both synchronous and asynchronous eHealthcare modalities are required now and in the future in order to determine how best to incorporate these modalities into patient-centred, collaborative care models. Resources must be dedicated to studies that evaluate the feasibility, acceptability and effectiveness of eHealth-enhanced models of IBD care to improve efficiency and cost-effectiveness, while increasing quality of life for persons living with IBD. Crohn's and Colitis Canada will continue to play a major leadership role in advocating for the health care delivery models that improve the quality of life for persons living with IBD.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".