Patient trust-in-physician and race are predictors of adherence to medical management in inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND: Adherence plays an important role in the therapeutic effectiveness of medical therapy in inflammatory bowel disease (IBD). We assessed whether trust-in-physician and Black race were predictors of adherence. METHODS: We performed a cross-sectional study of Black (n = 120) and White (n = 115) IBD patients recruited from an outpatient IBD clinic. Self-reported adherence to taking medication and keeping appointments, trust-in-physician, and health-related quality of life were measured using the validated instruments, the modified Hill-Bone Compliance Scale (HBCS), the Trust-in-Physician Scale (TIPS), and the Short IBD Questionnaire (SIBDQ), respectively. RESULTS: Overall adherence was 65%. Higher adherence correlated with greater trust-in-physician (r = -0.30; P < 0.0001), increasing age (r = -0.19; P = 0.01), and worsening health-related quality of life (r = -0.18; P = 0.01). Adherence was also higher among White IBD patients compared to Blacks (HBSC: 15.6 versus 14.0, P < 0.0001). Trust-in-physician, race, and age remained predictors of adherence to medical management after adjustment for employment, income, health insurance, marital and socioeconomic status, and immunomodulator therapy. The adjusted odds ratio for adherence in Blacks compared to Whites was 0.29 (95% confidence interval: 0.13-0.64). Every half standard deviation increase in trust-in-physician and every incremental decade in age were associated with 36% and 47% higher likelihood of adherence, respectively. CONCLUSIONS: Trust-in-physician is a potentially modifiable predictor of adherence to IBD medical therapy. Black IBD patients exhibited lower adherence compared to their White counterparts. Understanding the mechanisms of these racial differences may lead to better optimization of therapeutic effectiveness.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".