The Impact of the COVID-19 Pandemic on IBD Care in Alberta: Patient and Provider Perspectives
Bibliographic record
Abstract
Objective: The COVID-19 pandemic necessitated changes in the delivery of ambulatory care for patients with inflammatory bowel disease (IBD), including transitioning many visits to virtual formats and delaying non-urgent assessments. We aimed to evaluate the impact of the COVID-19 pandemic on IBD patient care from health care providers' (HCP) and patients' perspectives. Methods: We administered a 42-question HCP survey and a 44-question patient survey, which evaluated HCP and patient experience and satisfaction with care delivery and delays in access to IBD care during the first wave of the COVID-19 pandemic. Results: Surveys were completed by 19.2% (24/125) HCPs and 25.8% (408/1581) patients. Overall, 82.7% of patients with IBD maintained their care without disruption. The majority of patients were satisfied with a transition to virtual care. All HCPs were willing to use virtual care in the future; however, 60% (14/24) of HCPs reported that virtual care was not equivalent to in-person visits. Patients reported concerns around access to health resources, the uncertainty of IBD-specific care, and fear and stress due to employment uncertainty and safety. Providers also reported concerns about patient safety, patient education, adequate remuneration and challenges with providing care for new patients on virtual platforms. Conclusion: While some delays in health care delivery occurred during the first wave of the pandemic, both patients and HCPs were satisfied with a transition to new models of care delivery. These models may remain in place post-pandemic and allow for flexibility in care delivery that is acceptable to both patients and HCPs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".