Developing and Evaluating a Bundled Digital Tool to Improve Complex Care and Self-management of Patients with Inflammatory Bowel Disease: Protocol for a Hybrid Effectiveness-Implementation Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Individuals with inflammatory bowel disease (IBD) require comprehensive care to address the physical and psychosocial burden of their disease. The demand for IBD care often exceeds availability, resulting in delayed access and suboptimal management. Self-management tools can help address this gap by empowering patients to be more engaged in managing their disease. OBJECTIVE To design and implement a bundled digital health tool, MyIBDToolkit, to improve the quality of care and self-management for patients with IBD. METHODS A bundled digital health tool, MyIBDToolkit, will be integrated into our provincial electronic health record system. Using a type 2 hybrid effectiveness-implementation design, we will evaluate the effect of MyIBDToolkit on reach, effectiveness, adoption, implementation, and maintenance. We will use healthcare administrative records to assess outcomes in these five areas, including healthcare utilization and access to IBD care. We aim to reach 10,000 patients across Alberta, Canada, within 3 years. RESULTS In preparation for the pilot launch, we have identified key stakeholders and developed strategies to assess their readiness for MyIBDToolkit. We are also collecting mixed-methods data from patients to explore potential barriers and facilitators to using MyIBDToolkit. The first phase of MyIBDToolkit’s launched in October 2024. CONCLUSIONS MyIBDToolkit can potentially reduce the burden of IBD on patients, providers, and the healthcare system. By evaluating the effectiveness and the implementation of MyIBDToolkit, we aim to achieve immediate and sustained improvements to IBD care in Alberta.
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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 | 0.063 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.061 | 0.010 |
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".