Review: evidence on the effectiveness of interventions to improve patient adherence to prescribed medications is limitedCommentary
Bibliographic record
Abstract
R B Haynes Professor R B Haynes, McMaster University, Hamilton, Ontario, Canada; bhaynes@mcmaster.ca Are interventions to improve patient adherence to self- administered prescribed medications effective? Studies selected evaluated interventions to improve adherence to medications prescribed for medical disorders (including mental but not addiction disorders), had ⩾80% follow-up in each study group, reported both medication adherence and treatment outcomes, and had ⩾6 month follow-up in trials of long-term treatments that had positive initial results. Medline, CINAHL, EMBASE/Excerpta Medica, Cochrane Library , International Pharmaceutical Abstracts, PsycINFO, and Sociological Abstracts (all to Jan 2007); and reference lists were searched for randomised controlled trials (RCTs). Authors of relevant trials and reviews were contacted. 78 RCTs ({93}* unconfounded interventions, 10 with short-term treatment and {83}* with long-term treatment; n = 32–1113) met the selection criteria; 20 reported concealment of allocation. Conditions studied included asthma or chronic …
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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 | 0.012 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".