Barriers to pharmacist prescribing: a scoping review comparing the UK, New Zealand, Canadian and Australian experiences
Bibliographic record
Abstract
OBJECTIVES: Non-medical prescribers, including pharmacists, have been found to achieve comparable clinical outcomes with doctors for certain health conditions. Legislation supporting pharmacist prescribing (PP) has been implemented in the United Kingdom (UK), Canada and New Zealand (NZ); however, to date, Australian pharmacists have not been extended prescribing rights. The purpose of this review was to describe the barriers to PP found in the literature from the UK, Canada, NZ and Australia, and examine the implications of these for the development of PP in Australia. METHODS: We conducted a scoping review, which included peer-reviewed and grey literature, and consultation with stakeholders. Sources - Scopus, PubMed and CINAHL; Google Scholar, OpenGrey and organisational websites from January 2003 to March 2018 in the UK, Canada, NZ and Australia. Inclusion criteria - articles published in English, related to implementation of PP and articulated barriers to PP. KEY FINDINGS: Of 863 unique records, 120 were reviewed and 64 articles were eligible for inclusion. Three key themes emerged: (1) Socio-political context, (2) Resourcing issues and (3) Prescriber competence. The most common barriers were inadequate training regarding diagnostic knowledge and skills, inadequate support from authorities and stakeholders, and insufficient funding/reimbursement. CONCLUSIONS: If implementation of PP is to occur, attention needs to be focused on addressing identified barriers to PP implementation, including fostering a favourable socio-political context and prescriber competence. As such, a concerted effort is required to develop clear policy pathways, including targeted training courses, raising stakeholder recognition of PP and identifying specific funding, infrastructure and resourcing needs to ensure the smooth integration of pharmacist prescribers within interprofessional clinical teams.
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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 | 0.025 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.030 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| 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".