Understanding adherence to medications in type 2 diabetes care and clinical trials to overcome barriers: a narrative review
Bibliographic record
Abstract
AIM: To identify factors affecting adherence to medications in type 2 diabetes (T2D) care and clinical trials. BACKGROUND: Adherence to medication is associated with better patient outcomes, lower healthcare costs, and improved quality and robustness of trial data. In T2D, non-adherence to regimens may compromise glycemic, blood pressure and lipid control, which can, in turn, increase morbidity and mortality rates. DESIGN: A literature search was performed to identify studies reporting adherence to medications and highlighting specific adherence challenges/approaches in T2D. The search was limited to clinical trials, comparative studies or meta-analyses, reported in English with a freely available abstract. DATA SOURCE: MEDLINE (31 December 2008 to 31 December 2013). REVIEW METHODS: Studies not reporting adherence to medications or highlighting adherence challenges/approaches in T2D, presenting only self-reported adherence or including fewer than 100 patients were excluded. Eligible reports are discussed narratively. RESULTS: Factors identified as having a detrimental impact on adherence were smoking, depression and polypharmacy. Conversely, increased convenience (e.g. pen compared with vial and syringe; medication supplied by mail order vs. retail pharmacy) was associated with better patient adherence, as were interventions that increased patient motivation (e.g. individualized, nurse-led consultation) and education. CONCLUSIONS: Medication adherence is influenced by complex and multifactorial issues, which can include smoking, depression, polypharmacy, convenience of obtaining and administering the medication, patient motivation and education. We recommend simplifying treatment regimens, where possible, improving provider-patient communication, and providing support and education to increase medication adherence, with a view to improving patient outcomes and clinical trial data quality.
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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 | 0.036 | 0.194 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".