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Record W4396235331 · doi:10.1093/ijpp/riae013.009

An umbrella review of pharmacist prescribing: stakeholders’ views and impact on patient outcomes

2024· article· en· W4396235331 on OpenAlexaboutno aff
Bernadette Brennan, Judith Strawbridge, Derek Stewart, Cathal Cadogan, Jessica Eustace‐Cook, Mark R. Lowrey, Anne‐Marie Brady, Cristín Ryan

Bibliographic record

VenueInternational Journal of Pharmacy Practice · 2024
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePharmacistFamily medicineNursingPharmacy

Abstract

fetched live from OpenAlex

Abstract Introduction There is great geographical diversity in the degree of prescribing rights pharmacists have. In some countries such as the United Kingdom (UK), pharmacists have unrestricted prescribing rights following appropriate training; in other countries, pharmacists cannot legally prescribe. The impact of pharmacist prescribing (PP) has been studied in various qualitative and quantitative research studies, but this evidence has not been synthesised to support the development of PP internationally. Aim The aim of this umbrella review was to describe the international PP models, establish the impact of PP on patient outcomes and describe key stakeholders’ views of PP, by synthesising all the available systematic reviews (SRs). Methods Six databases (Embase, CINAHL, MEDLINE, Web of Science, Cochrane Library and PsycINFO) were searched, from January 2003 to June 2023, for key terms such as ‘pharmacist’, ‘prescribing’, ‘prescription’ and ‘systematic review’, ‘meta-analysis’, ‘meta-synthesis’. Systematic reviews that examined PP for any clinical condition, for patients of any age and in any healthcare setting were eligible for inclusion. SRs examining pharmacists’ interventions other than PP were excluded as were articles not published in English. Two researchers independently screened titles, abstracts and full texts for eligibility; discrepancies were resolved by discussion with a third reviewer. A data extraction tool was developed, piloted and refined prior to data extraction based on guidance from PRIOR[1] and the Joanna Briggs Institute (JBI).[2] Extracted information included: SR characteristics; inclusion and exclusion criteria; description of prescribing models, setting, clinical conditions, clinical and health service utilisation outcomes and key stakeholders’ views of PP. Data have been synthesised narratively due to the heterogeneity of included studies. Results A total of 6439 titles were retrieved from searches, with 917 duplicates removed. Following abstracts and full text screening (5522 and 129 respectively), 39 systematic reviews (8 qualitative; 19 quantitative and 12 mixed-methods) were included. The description of PP models varied within and between the SRs and demonstrated implementation of different PP models globally. Collaborative practice agreement with a physician, dependant prescribing by protocol and/or formularies were common in Canada and the United States, while supplementary prescribing and independent prescribing models were described in the UK. Prescribing activity was evident in all care settings and for a wide range of clinical conditions. SRs reporting on targeted clinical conditions, noted that patients of pharmacist prescribers had similar blood pressure control and depressive symptoms, better cholesterol and blood glucose control and reduced pain intensity when compared with patients of medical prescribers. SRs also suggests that pharmacist prescribers make fewer errors than non-pharmacists. Key stakeholders noted largely positive views towards PP, with a reduction in physician workload, an improvement in pharmacist job satisfaction, better utilisation of pharmacist knowledge and skills noted. Concerns over pharmacists’ diagnostic abilities, legal accountability for errors and appropriate implementation of PP models were noted. Conclusion A variety of PP models exist internationally. Careful examination of each model should be undertaken prior to adoption and implementation in countries where PP is currently not permitted. References 1. Gates M, Gates A, Pieper D et al. Reporting guideline for overviews of reviews of healthcare interventions: development of the PRIOR statement. BMJ 2022;378:e070849 2. Aromataris E, Fernandez R, Godfrey CM et al. Summarizing systematic reviews: methodological development, conduct and reporting of an umbrella review approach. Int J Evid Based Healthc. 2015 Sep;13(3):132-40.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.402
GPT teacher head0.551
Teacher spread0.149 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations5
Published2024
Admission routes1
Has abstractyes

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