AI Article Synopsis

Article Abstract

The Alliance of Independent Academic Medical Centers (AIAMC) organized and coordinated a multicenter learning collaborative, National Initiative V (NI V), focused on community health and health inequity. A pre-post descriptive study was designed to examine the outcomes of the AIAMC NI V. Data were collected from pre- and post-assessment surveys as well as a project milestone self-assessment survey. Twenty-nine institutions participated. By the conclusion of the NI, the majority of institutions had completed at least 1 of the milestones in each of the pre-work/background (65.52%), measurement (62.07%), methods (62.07%), and implement/sustain (20.69%) domains. Institutions reported a significant association between their readiness assessments prior to the start of the NI compared with their status of activities on completion. Milestone achievement is significantly associated with 3 of the assessment items. Learning collaboratives with thoughtfully integrated structure and support can be impactful on topic readiness for the participating organizations.

Download full-text PDF

Source
http://dx.doi.org/10.1177/1062860619877941DOI Listing

Publication Analysis

Top Keywords

learning collaboratives
8
collaboratives medical
4
medical education
4
education exploring
4
exploring impact
4
impact collaboratives'
4
collaboratives' structure
4
structure resources
4
resources teams'
4
teams' experience
4

Similar Publications

Interprofessional teaching rounds are a practical application of interprofessional education in bedside teaching, yet there is a lack of research on how interprofessional teaching rounds should be implemented into medical education. This study aimed to describe our experience in developing and implementing interprofessional teaching rounds during a clerkship rotation for medical students, and compares its strengths and weaknesses relative to traditional teaching rounds. Medical students were assigned to either the interprofessional teaching round group ( = 24) or the traditional teaching round group ( = 25), and each group participated in their assigned type of teaching round.

View Article and Find Full Text PDF

Background: Educational innovation in health professional education is needed to keep up with rapidly changing healthcare systems and societal needs. This study evaluates the implementation of PACE, an innovative curriculum designed by the physiotherapy department of the HAN University of Applied Sciences in The Netherlands. The PACE concept features an integrated approach to learning and assessment based on pre-set learning outcomes, personalized learning goals, flexible learning routes, and programmatic assessment.

View Article and Find Full Text PDF

Integrins identified as potential prognostic markers in osteosarcoma through multi-omics and multi-dataset analysis.

NPJ Precis Oncol

January 2025

Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co-constructed by the Province and Ministry, Guangxi Medical University, Nanning, Guangxi, China.

Osteosarcoma represents 20% of primary malignant bone tumors globally. Assessing its prognosis is challenging due to the complex roles of integrins in tumor development and metastasis. This study utilized 209,268 osteosarcoma cells from the GEO database to identify integrin-associated genes using advanced analysis methods.

View Article and Find Full Text PDF

Papermaking wastewater consists of a sizable amount of industrial wastewater; hence, real-time access to precise and trustworthy effluent indices is crucial. Because wastewater treatment processes are complicated, nonlinear, and time-varying, it is essential to adequately monitor critical quality indices, especially chemical oxygen demand (COD). Traditional models for predicting COD often struggle with sensitivity to parameter tuning and lack interpretability, underscoring the need for improvement in industrial wastewater treatment.

View Article and Find Full Text PDF

This article details the development of a next-word prediction model utilizing federated learning and introduces a mechanism for detecting backdoor attacks. Federated learning enables multiple devices to collaboratively train a shared model while retaining data locally. However, this decentralized approach is susceptible to manipulation by malicious actors who control a subset of participating devices, thereby biasing the model's outputs on specific topics, such as a presidential election.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!