Review: evidence on the effectiveness of interventions to improve patient adherence to prescribed medications is limitedCommentary
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it