Exploring the Experiences of Virtual Inflammatory Bowel Disease Care in Saskatchewan
Bibliographic record
Abstract
BACKGROUND: Individuals with inflammatory bowel disease (IBD) require life-long interactions with the healthcare system. Virtual care (VC) technologies are becoming increasingly utilized for accessing healthcare services. Research related to the use of VC technology for the management of IBD in Canada is limited. This study aimed to examine the VC experiences from the perspectives of individuals with IBD and gastroenterology care providers (GCPs). METHODS: A patient-oriented, qualitative descriptive approach was used. Semi-structured interviews were completed with individuals with IBD and GCPs. Data were analyzed using an inductive content analysis approach. RESULTS: A total of 25 individuals with IBD and five GCPs were interviewed. Three categories were identified: benefits of virtual IBD care delivery, challenges of virtual IBD care delivery, and optimizing IBD care delivery. Individuals with IBD and GCPs were satisfied with the use of VC technology for appointments. Participants believed VC was convenient and allowed enhanced access to care. However, VC was not considered ideal in some instances, such as during disease flares or first appointments. Thus, a blended use of virtual and in-person appointments was suggested for individualized care. CONCLUSIONS: The virtual method of connecting patients and providers is deemed useful for routine appointments and for persons living in rural areas. VC is becoming more common in the IBD care environment. Nurses are in a key position to facilitate and enhance virtual IBD care delivery for the benefit of both individuals living with IBD and providers.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".