A systematic review of pharmacists’ interventions to support medicines optimisation in patients with visual impairment
Bibliographic record
Abstract
Background People with visual impairment often report poorer health and encounter many challenges when using medicines. Pharmacists can play a significant role in optimising medicines use for these patients. However, little is known about pharmacists' current practices when providing services to this population nor the impact of such services, if any, on medicines optimisation-related outcomes. Aim of the review This systematic review aims to identify the types, and assess the effectiveness of, interventions provided by pharmacists on medicines optimisation-related outcomes. Method Systematic searches of the following electronic databases were carried out from date of inception to March 2018: Cochrane Library; MEDLINE; EMBASE; International Pharmaceutical Abstracts; Scopus; and Cumulative Index to Nursing and Allied Health Literature. Several trial registries and grey literature resources were also searched. Any randomised controlled trials, non-randomised controlled trials, controlled before-and-after studies, or interrupted time series analyses reporting on interventions provided by pharmacists to adult visually impaired patients and/or their caregivers in order to improve medicines optimisation-related outcomes of medicine safety, adherence, patient satisfaction, shared decision making, or quality of life were included. Results A total of 1877 titles/abstracts were screened, and 27 full text articles were assessed for eligibility. On examination of full texts, no studies met the inclusion criteria for this review. Conclusion This review highlights the need for future research that would be vital for promoting the safe and effective use of medicines and the delivery of pharmaceutical care services to people with visual impairment.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".