Exploring the barriers and facilitators to non-medical prescribing experienced by pharmacists and physiotherapists, using focus groups
Notice bibliographique
Résumé
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.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,024 | 0,042 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,004 |
| Communication savante | 0,002 | 0,004 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».