Factors associated with medication adherence in older patients: A systematic review
Bibliographic record
Abstract
OBJECTIVE: Medication adherence is a major challenge in the treatment of older patients; however, they are under-represented in research. We undertook a systematic review focused on older patients to assess the reasons underlying non-adherence in this population. METHODS: We searched multiple electronic databases for studies reporting reasons for non-adherence to medication regimens in patients aged 75 years and over. Our results were not limited to specific diseases, health-care settings, or geographical locations. The quality of eligible studies was assessed using the Newcastle-Ottawa Scale. A narrative synthesis of findings was performed. RESULTS: A total of 25 publications were included, all of which were in community settings. Frequent medication review and knowledge regarding the purpose of the medication were positively associated with adherence. Factors associated with poor adherence were multimorbidity, cognitive impairment, complex regimens with multiple prescribing physicians, and problems with drug storage or formulation. CONCLUSION: These findings suggest that interventions to improve adherence could focus on medication review aimed at simplifying regimens and educating patients about their treatment. Groups with poor adherence that may benefit most from such a model include patients with multiple comorbidities and cognitive impairment.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".