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Enregistrement W2093552748 · doi:10.1186/1472-6920-12-110

Improving primary care in British Columbia, Canada: evaluation of a peer-to-peer continuing education program for family physicians

2012· article· en· W2093552748 sur OpenAlexaffabout
Dan MacCarthy, Liza Kallstrom, Helena Kadlec, Marcus J. Hollander

Notice bibliographique

RevueBMC Medical Education · 2012
Typearticle
Langueen
DomaineHealth Professions
ThématiquePrimary Care and Health Outcomes
Établissements canadiensCollege of Veterinarians of British Columbia
Organismes subventionnairesnon disponible
Mots-clésContinuing medical educationMedical educationSession (web analytics)MedicineGovernment (linguistics)Primary careFamily medicinePsychologyNursingContinuing education

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: An innovative program, the Practice Support Program (PSP), for full-service family physicians and their medical office assistants in primary care practices was recently introduced in British Columbia, Canada. The PSP was jointly approved by both government and physician groups, and is a dynamic, interactive, educational and supportive program that offers peer-to-peer training to physicians and their office staff. Topic areas range from clinical tools/skills to office management relevant to General Practitioner (GP) practices and "doable in real GP time". PSP learning modules consist of three half-day learning sessions interspersed with 6-8 week action periods. At the end of the third learning session, all participants were asked to complete a pen-and-paper survey that asked them to rate (a) their satisfaction with the learning module components, including the content and (b) the perceived impact the learning has had on their practices and patients. METHODS: A total of 887 GPs (response rates ranging from 26.0% to 60.2% across three years) and 405 MOAs (response rates from 21.3% to 49.8%) provided responses on a pen-and-paper survey administered at the last learning session of the learning module. The survey asked respondents to rate (a) their satisfaction with the learning module components, including the content and (b) the perceived impact the learning has had on their practices and patients. The psychometric properties (Chronbach's alphas) of the satisfaction and impact scales ranged from .82 to .94. RESULTS: Evaluation findings from the first three years of the PSP indicated consistently high satisfaction ratings and perceived impact on GP practices and patients, regardless of physician characteristics (gender, age group) or work-related variables (e.g., time worked in family practice). The Advanced Access Learning Module, which offers tools to improve office efficiencies, decreased wait times for urgent, regular and third next available appointments by an average of 1.2, 3.3, and by 3.4 days across all physicians. For the Chronic Disease Management module, over 87% of all GP respondents developed a CDM patient registry and reported being able to take better care of their patients. After attending the Adult Mental Health module: 94.1% of GPs agreed that they felt more comfortable helping patients who required mental health care; over 82% agreed that their skills and their confidence in diagnosing and treating mental health conditions had improved; and 41.0% agreed that their frequency of prescribing medications, if appropriate, had decreased. Additionally for the Adult Mental Health module, a 3-6 month follow-up survey of the GPs indicated that the implemented changes were sustained over time. CONCLUSION: GP and medical office assistant participant ratings show that the PSP learning modules were consistently successful in providing GPs and their staff with new learning that was relevant and could be implemented and used in "real-GP-time".

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)0,003
score de la tête « metaresearch » (Gemma)0,007
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,690
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,007
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 savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,036
Tête enseignante GPT0,421
Écart entre enseignants0,385 · 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'étudeObservationnel
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

Citations22
Publié2012
Routes d'admission2
Résumé présentoui

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