A systematic review of general practice-based pharmacists’ services to optimize medicines management in older people with multimorbidity and polypharmacy
Bibliographic record
Abstract
BACKGROUND: Few studies have evaluated roles of general practice-based pharmacists (PBPs), particularly in optimizing medicines management for older people with both multimorbidity and polypharmacy. OBJECTIVE: To explore the types and effectiveness of services provided by PBPs, either alone or in collaboration with other primary health care professionals, that sought to optimize medicines management for older people with multimorbidity and polypharmacy. METHODS: Eight electronic databases and three trial registries were searched for studies published in English until April 2020. Inclusion criteria were randomized controlled trials, non-randomized controlled trials and controlled before-and-after studies of services delivered by PBPs in primary care/general practice, for patients aged ≥65 years with both multimorbidity and polypharmacy that focused on a number of outcomes. The Cochrane risk of bias tool for randomized trials (RoB 1) and the Risk of Bias in Non-randomized Studies-of Interventions (ROBINS-I) assessment tool were used for quality assessment. A narrative synthesis was conducted due to study heterogeneity. RESULTS: Seven studies met inclusion criteria. All included studies employed PBP-led medication review accompanied by recommendations agreed and implemented by general practitioners. Other patient-level and practice-level interventions were described in one study. The limited available evidence suggested that PBPs, in collaboration with other practice team members, had mixed effects on outcomes focused on optimizing medicines management for older people. Most included studies were of poor quality and data to estimate the risk of bias were often missing. CONCLUSION: Future high-quality studies are needed to test the effects of PBP interventions on a well-defined range of medicines management-related outcomes.
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.
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 | 0.019 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".