Exploring the implementation of pharmacist prescribing in Middle Eastern Arab countries: a scoping review
Bibliographic record
Abstract
Traditionally, the act of medication prescribing has been associated with physicians. The prescribing practice of healthcare professionals other than physicians is known as non-medical prescribing. Non-medical prescribers include pharmacists who have attained an advanced qualification in prescribing and are licensed by their professional regulatory bodies. Pharmacist prescribing is becoming more common in several countries and across care settings. Several countries have implemented pharmacist prescribing, such as the United Kingdom (UK), Canada, the United States, Australia and New Zealand. Over the last decade, the pharmacy profession has experienced a significant increase in the range of medications that pharmacists can prescribe.Currently, pharmacist prescribing is considered a novel area of practice in Middle Eastern Arab countries. The evolving nature of pharmacist prescribing, as evidenced by recent published research, underlines the importance of further exploration. To date, a limited number of published studies have investigated pharmacist prescribing in these countries. This scoping review is essential for gaining a deeper understanding of the role of pharmacists in the prescribing process in these countries, thereby contributing to the existing knowledge of pharmacy practice in this region. Furthermore, the availability of such evidence may inform the implementation of pharmacist prescribing practice in countries where this practice is in its infancy.This scoping review aims to explore the current state of pharmacist prescribing in Middle Eastern Arab countries, which will be accomplished through addressing the following objectives: identify which countries have implemented pharmacist prescribing and the required qualifications to gain the authority to prescribe; describe its implementation in terms of the models of prescribing being followed; and identify the barriers and facilitators that affect its implementation.The scoping review will follow the recommendations in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist. The review will include studies with a range of study designs, including quantitative, qualitative, mixed-methods studies, observational studies, surveys, interviews, focus groups, case studies, and systematic reviews.The studies will be included if they describe pharmacist prescribing in the following Middle Eastern Arab countries: Bahrain, Iraq, Jordan, Kuwait, Lebanon, Oman, Palestine, Qatar, Saudi Arabia, Syria, the United Arab Emirates, and Yemen. Studies can be published in English or Arabic. Studies will be excluded if they are focused on pharmacists’ other roles, conducted in Arabic-speaking countries in North Africa, and written in languages other than English or ArabicA comprehensive search will be conducted using the following five electronic databases: Medline, Embase, Scopus, Cochrane Library, and CINAHL, from the time of inception to August 2024. Potentially relevant grey literature will also be identified through targeted searches of dissertations/theses, and conference abstracts using Google Scholar, ProQuest, OpenGrey, and ProQuest Dissertations. The searches will be executed during August 2024, using search terms developed with input from a subject librarian from the Medical Library at Queen’s University Belfast.Two reviewers (RA and HM) will independently screen titles and abstracts of the eligible studies. Two reviewers (RA and CMH) will conduct full-text screening of the selected studies. In case of uncertainty about an article’s eligibility for inclusion, assistance from a third reviewer (HB) will be sought. A data charting table has been created using Microsoft Excel to compile the results of the scoping review. The data charting table will include the following: author, year of publication, aim, study design, study setting, country, model of prescribing, required qualifications, facilitators, barriers, and study recommendations. The use of the data extraction table has been tested on three studies before the data extraction process and refined accordingly.Descriptive analysis (frequency and percentages) will be used to present the synthesised results. This will provide a numerical overview of the type, number, and distribution of the included studies. Articles will be grouped according to the aim. A narrative report will be produced to summarise the charted data.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".