Interventions for helping patients to follow prescriptions for medications
Bibliographic record
Abstract
BACKGROUND: Efforts to assist patients with adherence to prescribed, self-administered medications might improve the benefits and efficiency of health care. OBJECTIVE: To update an ongoing review summarising the results of randomised controlled trials (RCTs) of interventions to help patients follow prescriptions for medications, focusing on trials that measured both adherence and clinical outcomes. SEARCH STRATEGY: Computerised searches to July 1998 in MEDLINE, CINAHL, The Cochrane Library, International Pharmaceutical Abstracts (IPA), PsychInfo, Sociofile, and HSTAR; bibliographies in articles on patient adherence; articles in the reviewers' personal collections; and contact with authors. SELECTION CRITERIA: Articles were selected if they reported an unconfounded RCT of an intervention to improve adherence with prescribed medications, measuring both medication adherence and treatment outcome, with at least 80% follow-up of each group studied and, for long-term treatments, at least six months follow-up for studies with positive initial findings. DATA COLLECTION AND ANALYSIS: Information on study design features, interventions and controls, and findings were extracted by one reviewer (PM) and a research assistant and confirmed by two of the other reviewers. The studies were too disparate to warrant meta-analysis. MAIN RESULTS: For short-term treatments, one study, of counselling and written information, showed an effect on adherence and clinical outcome. Ten of 19 interventions for long-term treatments reported in 17 RCTs were associated with improvements in adherence, but only nine interventions led to improvements in treatment outcomes. Almost all of the interventions that were effective for long-term care were complex, including combinations of more convenient care, information, counselling, reminders, self-monitoring, reinforcement, family therapy, and other forms of additional supervision or attention. Even the most effective interventions did not lead to large improvements in adherence and treatment outcomes. Two studies showed that telling patients about adverse effects of treatment did not affect their adherence. REVIEWER'S CONCLUSIONS: The full benefits of medications cannot be realised at currently achievable levels of adherence. Current methods of improving adherence for chronic health problems are mostly complex and not very effective. More studies of innovative approaches to assist patients to follow medication prescriptions 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 | 0.022 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".