Evaluation of a Simulation Based Workshop in an Anesthesia Residency Program

2018-06-11 01:09:10 | BioPortfolio

Published on BioPortfolio: 2018-06-11T01:09:10-0400

Clinical Trials [4551 Associated Clinical Trials listed on BioPortfolio]

A Comparative Study Between Simulation-based and Problem-based Learning in Difficult Airway Management Workshop

Abstract Background and Goal of study Teaching and learning in airway management are essential in anesthetic field. Though simulation-based and problem-based learning are sophisticated lea...

A Comparison of Clinical Simulation and Classical Learning for Airway Management in Medical Students

Multiple studies have shown clinical simulation benefits over classical learning method. Research on simulation of airway management has focused on endotracheal intubation training, while ...

Simulation-based and Problem-based Learning for Difficult Airway Management

Teaching and learning of difficult airway management are considered to be an essential skill in anesthesiology. As a result, doctors and nurses in anesthesia have to be vigilant in every s...

China OLIMPICS: China Ophthalmic Learning and Improvement Initiative in Cataract Surgery Trial

This is a prospective, investigator-masked randomised controlled education-intervention trial of intense simulation-based surgical education plus conventional training versus a current six...

Proficiency-Based Progression Training for Clinical Communication

Importance: Clinical communication is an important source of medical error and preventable adverse events. Objective: To determine the effectiveness of an online learning programme for IS...

PubMed Articles [39011 Associated PubMed Articles listed on BioPortfolio]

Use of simulation-based learning among perioperative nurses and students: A scoping review.

Simulation-based learning has been extensively explored, especially in baccalaureate nursing programmes. Recently, simulation-based learning has been introduced in perioperative nursing. The aim of th...

Applying Simulation Design Criteria to Non-Manikin-Based Experiences: A Modified ShadowBox Technique.

Creating simulation-based learning experiences is a process. With tools such as the 2016 INACSL Standards of Best Practice: Simulation Simulation Design and theories to guide development, nurse facult...

Self-Reported Emotions in Simulation-Based Learning: Active Participants vs. Observers.

Experiential learning through active participation is thought to be a key benefit of simulation-based education. Recent research has challenged this assumption, suggesting that active participants lea...

A reinforcement learning approach to personalized learning recommendation systems.

Personalized learning refers to instruction in which the pace of learning and the instructional approach are optimized for the needs of each learner. With the latest advances in information technology...

Build professional competence and Equip with strategies to empower midwifery students - An interview study evaluating a simulation-based learning course for midwifery educators in Bangladesh.

Use of simulation-based learning in midwifery education programmes is crucial. Due to midwifery educators in Bangladesh were lacking competence in using such pedagogical methods in their teaching, the...

Medical and Biotech [MESH] Definitions

Instructional use of examples or cases to teach using problem-solving skills and critical thinking.

Process in which individuals take the initiative, in diagnosing their learning needs, formulating learning goals, identifying resources for learning, choosing and implementing learning strategies and evaluating learning outcomes (Knowles, 1975)

Experience-based techniques for problem-solving, learning, and discovery that find a solution which is not guaranteed to be optimal, but sufficient for a given set of goals.

A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.

A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of unlabeled paired input-output training (sample) data.

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