The Influence of Motivational Interviewing on Patients With Inflammatory Bowel Disease: A Systematic Review of the Literature
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) is a condition accompanied by several physical and often psychological symptoms (e.g., depression). Treatments generally involve dietary modifications and prescription medications. Of concern, non-adherence rates with prescription medications for this population have been reported to be between 30% and 45%. In order to examine an intervention that has shown promise in improving adherence, researchers systematically reviewed the literature in order to determine the impact of a motivational interviewing (MI) intervention on outcomes for individuals diagnosed with IBD. The outcomes assessed were broad and included, among others, the target behaviors of medication adherence and advice-seeking, and also patient-perceived provider empathy. Results suggest that MI can be effective in improving outcomes for individuals with IBD since patients experienced improved adherence rates, displayed greater advice-seeking behavior, and perceived providers as having more empathy and better communication skills. Further research is required since the pool of retained studies is small, evidencing a paucity of literature focusing on this evidence-based health behavior intervention for the behaviors needed to optimally manage IBD. Further, only adults were examined in these studies, so generalizations to children and adolescents are limited.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".