Patients’ perspectives on medication for inflammatory bowel disease: a mixed-method systematic review
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) is a lifelong chronic disease that frequently requires long-term medical treatment to maintain remission. Patient perspectives on IBD medication are important to understand as nonadherence to IBD medication is common. We aim to synthesize the evidence about patients' perspectives on medication for IBD. A mixed-method systematic review was conducted on Scopus, EMBASE, Web of Science, and CINAHL. The convergent integrated approach to synthesis and integration of qualitative and quantitative findings was used for data analysis. Twenty-five articles from 20 countries were included in this review (20 quantitative, 3 qualitative, and 2 mixed-method studies). Patients have identified a lack of knowledge in the areas of efficacy, side effects, and characteristics of medications as key elements. Some negative views on IBD medication may also be present (e.g. the high number of pills and potential side effects). Lack of knowledge about medication for IBD was identified as a common issue for patients. Health services delivery for IBD should take into consideration these patients' perspectives. A focus on improving patient education in these areas could help empower patients and alleviate doubts resulting in better disease management and improved healthcare outcomes.
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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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".