Predictors of Medication Adherence in Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: This study reports cross-sectional medication adherence data from year 1 of the Manitoba Inflammatory Bowel Disease (IBD) Cohort Study, a longitudinal, population-based study of multiple determinants of health outcomes in IBD in those diagnosed within 7 yr. METHODS: A total of 326 participants completed a validated multi-item self-report measure of adherence, which assesses a range of adherence behaviors. Demographic, clinical, and psycho-social characteristics were also assessed by survey. Adherence was initially considered as a continuous variable and then categorized as high or low adherence for logistic regression analysis to determine predictors of adherence behavior. RESULTS: Using the cutoff score of 20/25 on the Medication Adherence Report Scale, high adherence was reported by 73% of men and 63% of women. For men, predictors of low adherence included diagnosis (UC: OR 4.42, 95% CI 1.66-11.75) and employment status (employed: OR 11.27, 95% CI 2.05-62.08). For women, predictors of low adherence included younger age (under 30 versus over 50 OR 3.64, 95% CI 1.41-9.43; under 30 vs. 40-49 yr: OR 2.62, 95% CI 1.07-6.42). High scores on the Obstacles to Medication Use Scale strongly related to low adherence for both men (OR 4.05, 95% CI 1.40-11.70) and women (OR 3.89, 95% CI 1.90-7.99). 5-ASA use (oral or rectal) was not related to adherence. For women, immunosuppressant use versus no use was associated with high adherence (OR 4.49, 95% CI 1.58-12.76). Low trait agreeableness was associated with low adherence (OR 2.03, 95% CI 1.12-3.66). CONCLUSIONS: Approximately one-third of IBD patients were low adherers. Predictors of adherence differed markedly between genders, although obstacles such as medication cost were relevant for both men and women.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".