Exploring the barriers and facilitators to non-medical prescribing experienced by pharmacists and physiotherapists, using focus groups
Bibliographic record
Abstract
BACKGROUND: Non-medical prescribing (NMP) was introduced into the United Kingdom to enhance patient care and improve access to medicines. Early research indicated that not all non-medical prescribers utilised their qualification. A systematic review described 15 factors influencing NMP implementation. Findings from a recent linked Delphi study with independent physiotherapist and pharmacist prescribers achieved consensus for 1 barrier and 28 facilitators. However, item ranking differed for pharmacist and physiotherapist groups, suggesting facilitators and barriers to NMP differ depending on profession. The aim of this study was to further explore the lived experiences of NMP by pharmacists and physiotherapists. METHOD: Study design and analytical approach were guided by Interpretative Phenomenology Analysis principles. Focus groups (November and December 2020) used the 'Zoom®' virtual platform with pharmacist and physiotherapist prescribers. Each focus group followed a topic guide, developed a priori based on the Delphi study results, and was audio recorded digitally. Transcripts underwent thematic analysis and data were visualised using a concept map and sunburst graph, and a table of illustrative quotes produced. Research trustworthiness was enhanced through critical discussion of the topic guide and study findings by the research group and by author reflexivity. The study is reported in line with COREQ guidelines. RESULTS: Participants comprised three physiotherapists and seven pharmacists. Five themes were identified. The most frequently mentioned theme was 'Staff', and the subtheme 'Clinical team', describing the working relationship between participants and team members. The other themes were 'Self', 'Governance', 'Practical aspects' and 'Patient care'. Important inter-dependencies were described between themes and subthemes, for example between 'Governance' and 'Quality and Safety'. Differences were highlighted between the professions, some relating to the way each profession practises (for example decision making), others to the way the prescribing role had been established (for example administration support). CONCLUSIONS: The key finding of collaborative working with the clinical team emphasises its impact on successful implementation of NMP. Themes may be inter-dependent, and inter-profession differences were identified. Specifically designed prescribing roles were beneficial for participants. For full NMP benefits to be realised all aspects of such roles must be fully scoped, before recruiting or training non-medical prescribers.
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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 | 0.024 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".