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Enregistrement W4396235331 · doi:10.1093/ijpp/riae013.009

An umbrella review of pharmacist prescribing: stakeholders’ views and impact on patient outcomes

2024· article· en· W4396235331 sur OpenAlexaboutno aff
Bernadette Brennan, Judith Strawbridge, Derek Stewart, Cathal Cadogan, Jessica Eustace‐Cook, Mark R. Lowrey, Anne‐Marie Brady, Cristín Ryan

Notice bibliographique

RevueInternational Journal of Pharmacy Practice · 2024
Typearticle
Langueen
DomaineMedicine
ThématiquePharmaceutical Practices and Patient Outcomes
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicinePharmacistFamily medicineNursingPharmacy

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction There is great geographical diversity in the degree of prescribing rights pharmacists have. In some countries such as the United Kingdom (UK), pharmacists have unrestricted prescribing rights following appropriate training; in other countries, pharmacists cannot legally prescribe. The impact of pharmacist prescribing (PP) has been studied in various qualitative and quantitative research studies, but this evidence has not been synthesised to support the development of PP internationally. Aim The aim of this umbrella review was to describe the international PP models, establish the impact of PP on patient outcomes and describe key stakeholders’ views of PP, by synthesising all the available systematic reviews (SRs). Methods Six databases (Embase, CINAHL, MEDLINE, Web of Science, Cochrane Library and PsycINFO) were searched, from January 2003 to June 2023, for key terms such as ‘pharmacist’, ‘prescribing’, ‘prescription’ and ‘systematic review’, ‘meta-analysis’, ‘meta-synthesis’. Systematic reviews that examined PP for any clinical condition, for patients of any age and in any healthcare setting were eligible for inclusion. SRs examining pharmacists’ interventions other than PP were excluded as were articles not published in English. Two researchers independently screened titles, abstracts and full texts for eligibility; discrepancies were resolved by discussion with a third reviewer. A data extraction tool was developed, piloted and refined prior to data extraction based on guidance from PRIOR[1] and the Joanna Briggs Institute (JBI).[2] Extracted information included: SR characteristics; inclusion and exclusion criteria; description of prescribing models, setting, clinical conditions, clinical and health service utilisation outcomes and key stakeholders’ views of PP. Data have been synthesised narratively due to the heterogeneity of included studies. Results A total of 6439 titles were retrieved from searches, with 917 duplicates removed. Following abstracts and full text screening (5522 and 129 respectively), 39 systematic reviews (8 qualitative; 19 quantitative and 12 mixed-methods) were included. The description of PP models varied within and between the SRs and demonstrated implementation of different PP models globally. Collaborative practice agreement with a physician, dependant prescribing by protocol and/or formularies were common in Canada and the United States, while supplementary prescribing and independent prescribing models were described in the UK. Prescribing activity was evident in all care settings and for a wide range of clinical conditions. SRs reporting on targeted clinical conditions, noted that patients of pharmacist prescribers had similar blood pressure control and depressive symptoms, better cholesterol and blood glucose control and reduced pain intensity when compared with patients of medical prescribers. SRs also suggests that pharmacist prescribers make fewer errors than non-pharmacists. Key stakeholders noted largely positive views towards PP, with a reduction in physician workload, an improvement in pharmacist job satisfaction, better utilisation of pharmacist knowledge and skills noted. Concerns over pharmacists’ diagnostic abilities, legal accountability for errors and appropriate implementation of PP models were noted. Conclusion A variety of PP models exist internationally. Careful examination of each model should be undertaken prior to adoption and implementation in countries where PP is currently not permitted. References 1. Gates M, Gates A, Pieper D et al. Reporting guideline for overviews of reviews of healthcare interventions: development of the PRIOR statement. BMJ 2022;378:e070849 2. Aromataris E, Fernandez R, Godfrey CM et al. Summarizing systematic reviews: methodological development, conduct and reporting of an umbrella review approach. Int J Evid Based Healthc. 2015 Sep;13(3):132-40.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,868
Score d'incertitude au seuil0,820

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,002
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,402
Tête enseignante GPT0,551
Écart entre enseignants0,149 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2024
Routes d'admission1
Résumé présentoui

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