Community Pharmacist Consultation Service: A Survey Exploring Factors Facilitating or Hindering Community Pharmacists’ Ability to Apply Learnt Skills in Practice
Bibliographic record
Abstract
BACKGROUND: The NHS Community Pharmacist Consultation Service (CPCS) offers patients requiring urgent care a consultation with a community pharmacist, following referral from general practice or urgent care. The study explored the impact of undertaking a Centre for Pharmacy Postgraduate Education (CPPE) CPCS learning programme, and barriers and enablers to CPCS delivery. METHODS: CPPE distributed an online survey to those who had undertaken their CPCS learning. The survey explored participants' knowledge, confidence and application of taught skills/tools, including clinical history-taking, clinical assessment, record keeping, transfer of care, and Calgary-Cambridge, L(ICE)F and SBARD communication tools. Details on barriers and enablers to CPCS delivery were also included. RESULTS: One-hundred-and-fifty-nine responses were received (response rate 5.6%). Knowledge of, and confidence in, taught skills were high and respondents reported applying skills in CPCS consultations and wider practice. Barriers to CPCS included a lack of general practice referrals, staffing levels, workload, and GP attitudes. Enablers included a clear understanding of what was expected, minimal concerns over indemnity cover and privacy, and positive patient attitudes towards pharmacy. CONCLUSION: This study demonstrates that community pharmacists can extend their practice and contribute to the enhanced provision of urgent care in England. This study identified barriers, both interpersonal and infrastructural, that may hinder service implementation.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".