Predictors of Medication Adherence in Pediatric Inflammatory Bowel Disease Patients at the Stollery Children’s Hospital
Bibliographic record
Abstract
BACKGROUND: Patients with inflammatory bowel disease (IBD) often do not take their medications as prescribed. OBJECTIVE: To examine self-reported adherence rates in IBD patients at the Stollery Children's Hospital (Edmonton, Alberta) and to determine predictors of medication adherence. METHODS: A survey was mailed to 212 pediatric IBD patients of the Stollery Children's Hospital. A chart review was completed for those who returned the survey. RESULTS: A total of 119 patients completed the survey. The nonresponders were significantly older than responders (14.5 years versus 13.2 years; P=0.032). The overall adherence rate was 80%. Nonadherence was associated with older age (14.6 years versus 13.0 years; P=0.04), longer disease duration (5.0 years versus 3.1 years; P=0.004) and reported use of herbal medications (40.0% versus 13.6%; P=0.029). The most common reasons reported for missing medications were forgetfulness, feeling better and too many medications. In addition, patients reported being more likely to take anti-inflammatory medications and less likely to take herbal medicines. CONCLUSION: Identified predictors of nonadherence such as age, disease duration and use of herbal treatments may enable the development of specific strategies to improve adherence in adolescents with IBD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".