Interventions to Enhance Patient Adherence to Medication Prescriptions
Bibliographic record
Abstract
CONTEXT: Low adherence with prescribed treatments is ubiquitous and undermines treatment benefits. OBJECTIVE: To systematically review published randomized controlled trials (RCTs) of interventions to assist patients' adherence to prescribed medications. DATA SOURCES: A search of MEDLINE, CINAHL, PSYCHLIT, SOCIOFILE, IPA, EMBASE, The Cochrane Library databases, and bibliographies was performed for records from 1967 through August 2001 to identify relevant articles of all RCTs of interventions intended to improve adherence to self-administered medications. STUDY SELECTION AND DATA EXTRACTION: Studies were included if they reported an unconfounded RCT of an intervention to improve adherence with prescribed medications for a medical or psychiatric disorder; both adherence and treatment outcome were measured; follow-up of at least 80% of each study group was reported; and the duration of follow-up for studies with positive initial findings was at least 6 months. Information on study design features, interventions, controls, and findings (adherence rates and patient outcomes) were extracted for each article. DATA SYNTHESIS: Studies were too disparate to warrant meta-analysis. Forty-nine percent of the interventions tested (19 of 39 in 33 studies) were associated with statistically significant increases in medication adherence and only 17 reported statistically significant improvements in treatment outcomes. Almost all the interventions that were effective for long-term care were complex, including combinations of more convenient care, information, counseling, reminders, self-monitoring, reinforcement, family therapy, and other forms of additional supervision or attention. Even the most effective interventions had modest effects. CONCLUSIONS: Current methods of improving medication adherence for chronic health problems are mostly complex, labor-intensive, and not predictably effective. The full benefits of medications cannot be realized at currently achievable levels of adherence; therefore, more studies of innovative approaches to assist patients to follow prescriptions for medications are needed.
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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 | 0.015 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".