Medication self-management interventions for persons with stroke: A scoping review.
Bibliographic record
Abstract
The use of multiple medications is common following a stroke for secondary prevention and management of co-occurring chronic conditions. Given the use of multiple medications post-stroke, optimizing medication self-management for this population is important. The objective of this scoping review was to identify and summarize what has been reported in the literature on interventions related to medication self-management for adults (aged 18+) with stroke. Electronic databases (Ovid Medline, Ovid Embase, EBSCO CINAHL, Ovid PsycINFO, Web of Science) and grey literature were searched to identify relevant articles. For inclusion, articles were required to include an adult population with stroke undergoing an intervention aimed at modifying or improving medication management that incorporated a component of self-management. Two independent reviewers screened the articles for inclusion. Data were extracted and summarized using descriptive content analysis. Of the 56 articles that met the inclusion criteria, the focus of most interventions was on improvement of secondary stroke prevention through risk factor management and lifestyle modifications. The majority of studies included medication self-management as a component of a broader intervention. Most interventions used both face-to-face interactions and technology for delivery. Behavioural outcomes, specifically medication adherence, were the most commonly targeted outcomes across the interventions. However, the majority of interventions did not specifically or holistically target medication self-management. There is an opportunity to better support medication self-management post-stroke by ensuring interventions are delivered across sectors or in the community, developing an understanding of the optimal frequency and duration of delivery, and qualitatively exploring experiences with the interventions to ensure ongoing improvement.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".