Predicting Noninsulin Antidiabetic Drug Adherence Using a Theoretical Framework Based on the Theory of Planned Behavior in Adults With Type 2 Diabetes
Bibliographic record
Abstract
Understanding the process behind noninsulin antidiabetic drug (NIAD) nonadherence is necessary for designing effective interventions to resolve this problem. This study aimed to explore the ability of the theory of planned behavior (TPB), which is known as a good predictor of behaviors, to predict the future NIAD adherence in adults with type 2 diabetes. We conducted a prospective study of adults with type 2 diabetes. They completed a questionnaire on TPB variables and external variables. Linear regression was used to explore the TPB's ability to predict future NIAD adherence, which was prospectively measured as the proportion of days covered by at least 1 NIAD using pharmacy claims data. The interaction between past NIAD adherence and intention was tested. The sample included 340 people. There was an interaction between past NIAD adherence and intention to adhere to the NIAD (P = 0.032). Intention did not predict future NIAD adherence in the past adherers and nonadherers groups, but its association measure was high among past nonadherers (β = 5.686, 95% confidence interval [CI] -10.174, 21.546). In contrast, intention was mainly predicted by perceived behavioral control both in the past adherers (β = 0.900, 95% CI 0.796, 1.004) and nonadherers groups (β = 0.760, 95% CI 0.555, 0.966). The present study suggests that TPB is a good tool to predict intention to adhere and future NIAD adherence. However, there was a gap between intention to adhere and actual adherence to the NIAD, which is partly explained by the past adherence level in adults with type 2 diabetes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".