Technology-mediated interventions for enhancing medication adherence
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
BACKGROUND: Despite effective therapies for many conditions, patients find it difficult to adhere to prescribed treatments. Technology-mediated interventions (TMIs) are increasingly being used with the hope of improving adherence. OBJECTIVE: To assess the effects of TMI, intended to enhance patient adherence to prescribed medications, on both medication adherence and clinical outcomes. METHODS: A secondary in-depth analysis was conducted of the subset of studies that utilized technology in at least one component of the intervention from an updated Cochrane review on all interventions for enhancing medication adherence. We included studies that clearly described an information and communication technology or medical device as the sole or major component of the adherence intervention. RESULTS: Thirty-eight studies were eligible for in-depth review. Only seven had a low risk of bias for study design features, primary adherence, and clinical outcomes. Eighteen studies used a TMI for education and/or counseling, 11 studies used a TMI for self-monitoring and/or feedback, and nine studies used electronic reminders. Studies used a variety of TMIs, with telephone the most common technology in use. Studies targeted a wide distribution of diseases and used a variety of adherence and clinical outcome measures. A minority targeted children and adolescents. Fourteen studies reported significant effects in both adherence and clinical outcome measures. CONCLUSIONS: This review provides evidence for the inconsistent effectiveness of TMI for medication adherence and clinical outcomes. These results must be interpreted with caution due to a lack of high-quality studies.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 it