Evolution in Pharmacy Education: Developing Effective Patient Care Practitioners
Notice bibliographique
Résumé
Changes outlined in the Blueprint for Pharmacy emphasize the need to provide “[o]ptimal drug therapy outcomes for Canadians through patient-centred care”. This approach requires pharmacists to contribute to outcomes-focused patient care, while working within health care teams; pharmacists also need to be accountable and responsible for the safe and effective use of medications. There is an urgent need for all pharmacists to take on this responsibility. Almost half of patients visiting a community pharmacy or a clinic will have a drug therapy problem. Patients who have been admitted to hospital also experience a substantial number of preventable adverse drug events. Medication costs represent the fastest-rising expenditure within our health care system. Furthermore, with increased use of medications, there is potential for even more adverse drug events to occur. Also, the elderly population uses the most medications, exposing them to an even greater risk of drug therapy problems; in fact, by 2036, 1 in 4 Canadians will be over 65 years of age. Pharmacists are required to effectively manage drug therapy for all patients, and both faculties of pharmacy and the profession as a whole have obligations to prepare graduating pharmacists for this role. Education to prepare pharmacists for expanded patient care roles has started to change. For example, all pharmacy schools in Canada have made a commitment to have an entry-level PharmD curriculum in place by 2020. Some key components of new curricula include an expanded and integrated approach to teaching pharmacotherapy, management courses that include development of patient care services, incorporation of physical assessment and competencies related to the expanded scope of practice (e.g., training for administration of injections), and extensive clinical training in direct patient care. This expanded education is supported nationally by the 2010 educational outcomes of the Association of Faculties of Pharmacy of Canada, which call for pharmacy graduates to be “medication therapy experts”. Also, the Canadian Council for Accreditation of Pharmacy Programs (CCAPP) has now approved new standards for PharmD first professional degree programs, which will be implemented in early 2013. Each program must offer a total of 40 weeks (1600 h) of experiential education, with early and mid-program practice experiences lasting at least 8 weeks (320 h) and concluding practice experiences (at the end of the program) lasting at least 24 weeks (960 h). This level of clinical training is considered essential for graduates to become independent, competent patient care practitioners, who are responsible to both patients and colleagues within interprofessional teams. Current training, although effective, is demanding for the preceptor and the site and does not explicitly hold the student accountable to the patient and the rest of the health care team. There are many challenges and just as many opportunities as we consider a paradigm shift in how we train pharmacy students. At the June 2011 meeting of the American Society of Health-System Pharmacists, Ashby proposed a pharmacy student training model that would enable trainees to become more accountable and make them indispensable within the patient care team. He also emphasized the importance of students undertaking patient care activities that are known to improve patient outcomes. Additional recommendations included extension of rotational experiences and provision of support to students in their own desired career paths by matching training to interests. In this issue of the CJHP, Hall and others provide an insightful overview of current experiential training in hospital pharmacy and put forward 8 guiding principles that they consider as key factors for the success of future experiential models. Much of what they outline is akin to training within medical programs, including starting experiential education earlier in the program; providing a mix of hospital and community site experience early, with sustained practices in later years
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 | 0,046 | 0,053 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,006 | 0,007 |
| Communication savante | 0,012 | 0,018 |
| Science ouverte | 0,004 | 0,015 |
| Intégrité de la recherche | 0,011 | 0,016 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,006 |
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 ».