Medication Supports at Transitions Between Hospital and Other Care Settings: A Rapid Scoping Review
Bibliographic record
Abstract
PURPOSE: Transitions in care (TiC) often involves managing medication changes and can be vulnerable moments for patients. Medication support, where medication changes are reviewed with patients and caregivers to increase knowledge and confidence about taking medications, is key to successful transitions. Little is known about the optimal tools and processes for providing medication support. This study aimed to identify describe patient or caregiver-centered medication support processes or tools that have been studied within 3 months following TiC between hospitals and other care settings. METHODS: Rapid scoping review; English-language publications from OVID MEDLINE, OVID EMBASE, Cochrane Library and EBSCO CINAHL (2004-July 2019) that assessed medication support interventions delivered within 3 months following discharge were included. A subset of titles and abstracts were assessed by two reviewers to evaluate agreement and once reasonable agreement was achieved, the remainder were assessed by one reviewer. Eligibility assessment for full-text articles and data charting were completed by an experienced reviewer. RESULTS: A total of 7671 unique citations were assessed; 60 studies were included. Half of the studies (n = 30/60) were randomized controlled trials. Most studies (n = 45/60) did not discuss intervention development, particularly whether end users were involved in intervention design. Many studies (n = 37/60) assessed multi-component interventions with written/print and verbal education components. Few studies (n = 5/60) included an electronic component. Very few studies (n = 4/60) included study populations at high risk of adverse events at TiC (eg, people with physical or intellectual disabilities, low literacy or language barriers). CONCLUSION: The majority of studies were randomized controlled trials involving verbal counselling and/or physical document delivered to the patient before discharge. Few studies involved electronic components or considered patients at high-risk of adverse events. Future studies would benefit from improved reporting on development, consideration for electronic interventions, and improved reporting on patients with higher medication-related needs.
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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 | 0.032 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.035 | 0.030 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".