Community pharmacist consultation service: survey insights into impact of learning on practice, and barriers and enablers to service implementation
Notice bibliographique
Résumé
Abstract Introduction Some patients attend NHS services with ‘clinically divertible’ urgent low acuity conditions. The NHS Community Pharmacist Consultation Service (CPCS) enables the referral of such patients and subsequent consultation with a community pharmacist. The Centre for Pharmacy Postgraduate Education (CPPE) offered a learning programme to prepare pharmacists for a more clinical and person-centred approach when delivering the CPCS. Aim To investigate the impact of the CPPE CPCS learning programme on learnt skills and their application in practice, and barriers and enablers to CPCS delivery. Methods A survey was designed to explored participants’ knowledge, confidence and application of taught skills/tools, including clinical history-taking, clinical assessment, record-keeping, Calgary-Cambridge, L(ICE)F (lifestyle, ideas, concerns, expectations, feelings) and SBARD (situation, background, assessment, recommendation, decision) communication tools. Statements on barriers and enablers to CPCS delivery were included. In November/December 2021, CPPE emailed an online survey (one reminder) to CPCS learners who had agreed to be contacted (n=2836). The University of Manchester’s Research Ethics Committee decision tool confirmed that ethics committee approval was not required for this study. Results One-hundred and fifty-nine pharmacists responded (5.6%). Sixty percent were female, all ages were represented, the most populous groups being 55-64 (33.3%) and 45-54 years (27.7%). Ethnicity was broadly representative of community pharmacists: 49.0% white, 38.9% Asian, 8.2% black, and 1.9% Arab. Sixty-eight (43%) of respondents were working in a large multiple community pharmacy, 33.3% (n=53) in an independent pharmacy, and 17.0% (n=27) in a small to medium pharmacy multiple. Knowledge of, and confidence in, taught skills were high and respondents reported applying skills in CPCS consultations and wider practice. There was strong positive correlation between the perceived levels of competence and confidence when delivering CPCS (r=0.966, p=<0.001). The level of competence (r=0.259, p=0.003) and confidence increased (r=0.264, p=0.002) with an increasing number of NHS111 referrals. With regards to specific skills learnt, the highest levels of knowledge were recorded for ‘taking a clinical history’ (86% agreement), ‘clinically assessing a patient’ (84%), ‘using L(ICE)F’ (84%), and ‘completing an accurate and concise clinical record’ (81%). Seventy-three percent of respondents agreed they knew how to use the Calgary Cambridge, whilst only 49% knew how to use SBARD. Barriers to CPCS included lack of GP 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. Those working in independent pharmacies were more likely than those in multiple pharmacies to report that they were receiving GP referrals (68.5% vs. 49.0%, X2=5.249, p=0.022), that they had enough staff to provide the CPCS (45.3% vs. 26.3%, X2=5.526, p=0.019), and that the local GP considers community pharmacy to be an integral part of the primary healthcare team (54.7% vs. 37.2%, X2=4.214, p=0.040). Employed pharmacists were more likely than locums to report that they had a good relationship with their local general practice (57.5% vs. 37.5%, X2=5.436, p=0.020). Discussion/Conclusion This study demonstrates that CPPE learning contributed to community pharmacists’ extended knowledge and skills in CPCS delivery, which contributes to enhanced provision of urgent care in England. This study identified barriers, both interpersonal and infrastructural, that may hinder service implementation.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».