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Enregistrement W2565011050 · doi:10.5339/qfarc.2016.hbpp2507

Capturing the International Diversity of Pharmacists at Sidra Medical and Research Center and their Integration into the Qatar Community

2016· article· en· W2565011050 sur OpenAlexaboutno aff
Anish Patel, Maria Paiva, Hazar Alnifaidy

Notice bibliographique

RevueQatar Foundation Annual Research Conference Proceedings Volume 2016 Issue 1 · 2016
Typearticle
Langueen
DomaineMedicine
ThématiquePharmaceutical Practices and Patient Outcomes
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésStaffingPharmacyHealth careMedicineDemographicsNursingFamily medicineMedical educationPolitical scienceSociology

Résumé

récupéré en direct d'OpenAlex

Introduction Sidra Medical and Research Center (SMRC) is a green field ultramodern academic tertiary care hospital, which will bring a new model of integrated patient care to the women and children of Qatar, the Gulf region, and beyond. This model is based on North American standards and practices. SMRC encompasses three essential missions: world-class patient care, medical education, and biomedical research. To deliver this model and achieve these missions, experienced healthcare professionals, including pharmacists, have been recruited from across the world. SMRC's Department of Pharmacy has been working on multiple projects to help facilitate the opening of the hospital and outpatient clinic (OPC). Additionally, it has integrated pharmacists into the healthcare community of Qatar to forge partnerships with academic and healthcare facilities and gain understanding of the needs and culture of the population it will serve. Objectives 1. To describe the demographics and skillsets of pharmacists at SMRC. 2. To describe the integration of SMRC pharmacists into the Qatar community. 3. To highlight pharmacists’ involvement in the design, build, and commissioning of a green field ultramodern academic tertiary care center. Methods Participants were identified from a department staffing database. Department members that did not possess a pharmacy degree were excluded from the study. A web-based survey was created, piloted, and validated. Demographic parameters, education, and work related activities were captured. Participation was anonymous and voluntary. Ethics approval was not required for this abstract. Results SMRC currently employees 16 pharmacists, 15 out of 16 (94%) pharmacists responded to the survey with demographics and their respective engagements in the Qatar community. The majority of these pharmacists (66.7%) hold a doctorate of pharmacy degree, 27% hold a master degree in pharmacy, and the remaining pharmacists hold a bachelor degree of pharmacy. Over 50% of SMRC pharmacists were educated in the United States of America, 20% were educated in Canada, 20% in the United Kingdom, and the remainder educated in Egypt. All pharmacists attained their education in countries where they were national citizens. Prior to relocation in Qatar, all but two pharmacists practiced in the same country in which they received their pharmacy education. Over 50% of pharmacists fall into 31–40 year age range, 20% fall within 41–50 years of age, and two pharmacists are in the 20–30 year age bracket and one is above 50 years of age. The majority (60%) of pharmacists have been practicing for more than 10 years, and 33% have been practicing between five and ten years. Eighty percent of pharmacists hail from academic tertiary care hospitals and the remaining pharmacists hail from a variety of settings, including community/regional town/rural hospitals, outpatient or ambulatory care settings, and non-clinical (academia, administration, or informatics). Fifty three percent of pharmacists have now integrated into inpatient care at Hamad Medical Corporation (HMC) facilities. Pharmacists share their expertise with a variety of specialties including critical care, general pediatrics, internal medicine, infectious diseases, and neonatal intensive care. Activities include interdisciplinary patient-care rounds, delivering educational in-services, and providing input on pharmacy-related workflows. Over 26% are involved in non-clinical SMRC community projects such as teaching at Qatar University and building the computer physician order entry system in collaboration with HMC facilities. Two pharmacists are integrated into the outpatient/ambulatory care of HMC, where they help alleviate capacity demands whilst gaining an insight into dispensary logistics that could impact the workflow planned for SMRC. Two pharmacists are not practicing clinically; however they have facilitated in the planning and implementation of clinical and community projects. Three pharmacists were not integrated in the community as their expertise is required full-time at SMRC to complete project work. In addition to integrating into the Qatar community, pharmacists remain committed to achieving the vision and mission of SMRC. Project work includes building a drugs formulary, creating hospital workflows, modifying treatment order sets, developing treatment guidelines, and establishing avenues for pharmacist-led research. Health informatics and clinical pharmacists have been utilized in the design, build and test of an e-prescribing system suitable for both adult and pediatric (including neonatal) populations. Conclusion SMRC pharmacists hail from diverse educational and practice environments. Their diversity and their wealth of experience has been extensively and successfully integrated within the SMRC organization and community of Qatar. Through this integration, significant contributions have been made to the country's health and education, leading to improved patient care.

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)0,008
score de la tête « metaresearch » (Gemma)0,006
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies, Charge utile insuffisante (le modèle a refusé de juger)
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 enseignants0,766
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0080,006
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,002
Communication savante0,0000,001
Science ouverte0,0010,003
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,243
Tête enseignante GPT0,469
Écart entre enseignants0,226 · 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.

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

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

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