Author: collaborativemomentum

Creating Competency Statements

Creating Competency Statements for Your Nursing  Program: Models, Tips and Tools
Creating Competency Statements for Your Nursing Program
Susan Sportsman, PhD, RN, ANEF, FAAN

At some point, nearly every nurse educator has written a reference letter describing a student as “competent”. Likewise, when discussing students who are struggling, we might say “She hasn’t yet reached the expected level of competence.”  While we usually know what we mean by “competent”, it’s not always clear to others. This lack of clarity, along with the complexity of the current health care system, is driving the shift toward Competency-Based Education (CBE) in nursing programs.

Although some nursing programs—like other health professions —have incorporated competency-based approaches for many years, it has become more common. One catalyst was the AACN’s 2021 publication: The Essentials: Core Competencies for Professional Nursing Education. (https://www.aacnnursing.org/Portals/0/PDFs/Publications/Essentials-2021.pdf). Other nursing/accreditation organizations are also encouraging the use of competencies to prepare and evaluate nursing students. For example, NLN has published a Competency -Based Education Toolkit to support nursing faculty in implementing this approach. (https://www.aacnnursing.org/Portals/0/PDFs/Publications/Essentials-2021.pdf).

Competency-Based Education (CBE)

As promised in our February 2025 Collaborative Momentum Consulting blog, we’re diving deeper into CBE- starting with a discussion of competency statements. Traditionally, nursing programs have used program goals, or student learning outcomes, to define the knowledge, skills and attitudes students should demonstrate by graduation. However, these outcomes are frequently too broad or abstract to support reliable measurement. In addition, course objectives are often not aligned with program goals, making it difficult to accurately assess learners’ competency.

Clear, measurable exit competency statements help faculty develop desired competencies which articulate behaviors necessary for successful practice post-graduation. Once these exit competencies are established, more specific competence statements (often referred to as sub-competencies) can suggest assessment measures to evaluate learners’ abilities as they move through the course of study. This backward design of the curriculum provides a roadmap to guide learners and faculty in assessing the knowledge and behaviors necessary for practice.

How to Develop Effective Competency Statements in Nursing Education

Creating effective competency statements requires three essential actions: Continue reading “Creating Competency Statements”

Competency-Based Education in Nursing

In this episode we discuss the shift to CBE. Tune in to hear expert advice to guide your program forward. Listen on Spotify,   Apple Podcasts or YouTube. 


Competency-Based Education in Nursing: Nurse Educators Now Ep. 2 by Collaborative Momentum Consulting

Many nursing programs are working to integrate competencies into their curriculum to meet new accreditation standards — but the shift to competency-based education (CBE) can feel overwhelming. In this episode Susy talks with Dr. Jan Jones-Schenk, a nationally recognized expert in competency-based nursing education.

Dr. Jones-Schenk shares practical advice to help nurse educators align curricula with the AACN Essentials, meet accreditation requirements, and support student success through a CBE model. Whether you’re just starting or already deep into the transition, this episode offers clarity, direction, and expert insight you can trust.

Tune in and subscribe Nurse Educators Now on YouTube , Apple Podcasts, or Spotify  for expert insights and actionable advice to stay ahead in the ever-evolving world of nursing education.

#competencybasededucation #nurseeducator #nursingfaculty #nurseeducatorsnow #janjonesschenk #collaborativemomentumconsulting #susansportsman

We offer effective nursing education consulting services to programs throughout the U.S. Reach out and let us know what we can do for your program.

 

Competency-Based Education (CBE)

The Shift Toward Competency-Based Education

Competency-Based Education in Nursing
Susan Sportsman, PhD, RN, ANEF, FAAN
Introduction

The challenge of preparing novice nurses to deliver safe, effective care is not new. New graduates, faculty and practice partners alike have all recognized that safety should be the hallmark of a nurse entering the profession- regardless of their practice setting. However, in today’s complex health care environment, accurately assessing whether a novice nurse is truly prepared is no simple task. With increasing patient acuity, expanding scopes of practice, and evolving healthcare roles, nursing education must continuously adapt to ensure graduates have the necessary capabilities for real-world practice. For the last 25 years, a variety of efforts have been made by the profession to quantify specific competencies essential for nurses. Table A gives examples of efforts to evaluate the behaviors of a competent nurse in various roles and settings.

Table A:  Selected Efforts to Support Integration of Nursing Competency into PracticeIntegration of Nursing Competency into Practice

Current literature suggests the Nurse Competency Scale (both the original and shortened version) is the most commonly used self-assessment instrument to measure the generic competence of registered nurses. The scale, based on Benner’s from Novice to Expert model, is used to evaluate the nurse’s ability to integrate knowledge, skills, attitudes, and values in specific context. The nursing behaviors identified in the scale includes the nurse’s helping role, teaching-coaching, diagnostic functions, managing situations, therapeutic interventions, ensuring quality, and worker role. (Meretoja, et. al, 2004).

The Shift Toward Competency Based Nursing Education 

In response to the 2021 of American Association of Colleges of Nursing (AACN) The Essentials: Core Competencies for Professional Nursing Education, many baccalaureate and graduate nursing programs seeking accreditation by the Commission on Collegiate Nursing Education (CCNE) today are in the process of integrating Competency Based Education into their curriculum. AACN recognizes that complete integration of these competencies into baccalaureate and graduate education will take time. As a result, Continue reading “Competency-Based Education (CBE)”

Analyzing NGN NCLEX Exam Results

Analyzing NGN NCLEX Exam Results: Key Insights for Educators

NGN NCLEX Exam Results Collaborative Momentum Consulting
Susan Sportsman, PhD, RN, ANEF, FAAN

The National Council of State Boards of Nursing hosts an annual meeting to provide nurse educators with information regarding the results of NCLEX-RN and PN examinations. On September 12th, I joined the virtual audience to hear the most recent updates about the implementation of the NGN NCLEX. For those unable to attend the conference, I’m providing a brief update about this meeting as a guide to help you stay ahead in preparing students for future NCLEX Exams. Continue reading “Analyzing NGN NCLEX Exam Results”

Artificial Intelligence in Nursing Education: Exploring the Basics

artificial intelligence in nursing education
By Susan Sportsman, PhD, RN, ANEF, FAAN
Introduction

Change is inevitable. Some changes can be positive, while others may have negative consequences. Often change brings the potential for both. Individual perceptions usually shape whether we view the anticipated outcomes of a particular change as positive or negative. A perfect example of differing perspectives of a new innovation is the expanding use of artificial intelligence technology in nursing education. Many nurse educators believe this technology has the potential to transform education by providing more personalized and efficient learning experiences for students (DeGagne, 2023). Despite this optimism, others are fearful about the rapid pace of AI innovation and the lack of knowledge related to the potential risks and unintended consequences of this technology (Glauberman, 2023). Wickstrom (2024) suggests that nurse educators may not be integrating AI into their practice at a rapid rate because of a lack of nursing education research in this area.

Hesitancy regarding the faculty’s ability to develop competency in this area also contributes to negativity toward artificial intelligence in nursing education. De Gagne (2023) suggests that faculty may have concerns about the impact of AI on their workload (How long will it take to learn to use AI in the classroom or clinical?) and in their role as faculty (Will AI partly or completely replace my job?).

This hesitancy resonates with me. Although usually interested in trying new things, my limited experience in AI makes me apprehensive of ways that it might be used in nursing education. I suspect that I might not be alone in this concern, so over the next several months, the Collaborative Momentum Blog will attempt to de-mystify the use of AI, so those of us who are hesitant can feel more comfortable in using some form in nursing education.

First, Some Definitions

Below are some basic definitions to get us started.

Artificial Intelligence (AI)– a broad discipline of computer science that aims to develop systems capable of performing tasks that traditionally requires human intelligence (Shepherd, Griesheimer, 2024). AI is an umbrella term for any machine that can replace some aspect of human intelligence. The system uses inputs to reason, learn and process (Wickstrom, 2024). Types of AI include: Non-generative or traditional AI, which creates patterns and makes predictions and excels at analyzing data and performing specific tasks such as spam filtering and medical diagnoses. Generative AI, which focuses on creating new content based on the information used to train it, such as text, images and music (Shepherd, Griesheimer, 2024).

Machine-Learning– Computers can learn without human programing. Learning algorithms make predictions after identifying patterns and trends. Ultimately, they can program themselves through experience. Amazon shopping recommendations and Netflix suggestions are examples of machine-learning (Wickstrom, 2024).

Natural Language processing (NLP)- aims to bridge the gap between humans and machines by enabling them to communicate effectively through natural language. NLP uses advanced algorithms and techniques to process and analyze complex human language (Shepard, Griesheimer, 2024).

Large Language Model (LLM)- an advanced AI system program that is trained on huge data sets from many disparate sources, including the internet, to recognize and generate responses to questions and prompts. ChatGPT is an example of Large Language Model AI (Shepard, Griesheimer, 2024).

Prompt engineering– the deliberate and strategic formulation of instructions given to an AI system to produce the desired result. Prompt engineering works within a generative AI system to allow it to use past interactions to improve future content generation. It is similar to a Google search, except a Google search delivers links to information, and in generative AI, the process involves refining the question or command to ensure clarity and specificity, with the goal of more accurate and relevant responses (Shepard, Griesheimer, 2024).

Neural Network– a series of algorithms that seek to identify relationships in a data set via a process that mimics the way the human brain works.

Prediction Models– predicts best outcomes based on data form previous events, calculating probability of events based on earlier data on similar events and hidden trends. Examples pf nursing practice-related predictions include risk assessment of falls or skin breakdown risks (Wickstrom, 2024).

ChatGPT– an AI chatbot with natural language processing (NLP) which allows a human-like conversation to complete various tasks. This generative AI can answer questions, assist in composing, emails, essays, and code, among other things (https://www.zdnet.com/article/what-is-chatgpt-and-why-does-it-matter-heres-everything-you-need-to-know/).

Ways AI may Impact Students and Faculty

The list below describes ways that AI can enhance student learning and provide assistance to faculty in their work. Continue reading “Artificial Intelligence in Nursing Education: Exploring the Basics”