An Integrative Literature Review of Evidence-Based Teaching Strategies for Nurse Educators

Journal Club Article: Breytenbach, C., ten Ham-Baloyi, W., & Jordan, P. J. (2017). An Integrative Literature Review of Evidence-Based Teaching Strategies for Nurse Educators. Nursing Education Perspectives38(4), 193-197. [abstract]

Background

Evidence-based teaching strategies in nursing education are fundamental to promote an in-depth understanding of information. The teaching strategies of nurse educators should be based on sound evidence or best practice.

“To teach these skills, knowledge, behaviors, and attitudes, nurse educators must utilize a variety of teaching strategies that actively engage their students (Billings & Halstead, 2012).

Students, who are increasingly skilled in technology, benefit from a diversity of teaching strategies based on their needs, including experiential and active learning (Samarakoon, Fernando, & Rodrigo, 2013).”

The principles of adult learning where the strategies encourage and allow ownership for one’s own learning.

Method

Integrative literature review of sixteen studies.

Findings

Eight teaching strategies were identified:

  1. E-learning
  2. Concept mapping
  3. Internet-based learning (IBL)
  4. Web-based learning
  5. Gaming
  6. Problem-based learning (PBL)
  7. Case studies
  8. Evidence-based learning (EBL)

The following three strategies of concept mapping, IBL and EBL demonstrated the most increase in knowledge.

“Based on the findings from this review, the authors propose that multiple teaching strategies should be encouraged in a nursing curriculum to allow for the use of a set of strategies that are suitable for different learning styles and student needs.”

The authors recommend that nurse educators be trained to understand the different educational strategies and the benefits to learning that they offer to aid critical thinking, knowledge acquisition and decision making.

Conclusion

All teaching strategies enhanced the learning experience, but more research is needed. In summary a multi-modal approach to teaching and delivering content is required.

Keywords: Evidence-Based Teaching; integrative review; teaching; nurse educator.

Reference

Breytenbach, C., ten Ham-Baloyi, W., & Jordan, P. J. (2017). An Integrative Literature Review of Evidence-Based Teaching Strategies for Nurse Educators. Nursing Education Perspectives38(4), 193-197. [abstract]

Book Club: Daniel Goleman: Emotional Intelligence

Book Club: Goleman, D. (2006). Emotional Intelligence. Bantam. [Goodreads blurb]

Phew, this was a tough read for me, maybe a sign of my own emotional intelligence! I have decided not to add my notes and comments, instead provide some links to reviews that may expand on the theory discussed by Daniel Goleman and inspire you, like so many have been by this author. I have had to admit defeat on this one and say it didn’t bring many new ideas or inspiration for me, but will return to read again in the future.

“If your emotional abilities aren’t in hand, if you don’t have self-awareness, if you are not able to manage your distressing emotions, if you can’t have empathy and have effective relationships, then no matter how smart you are, you are not going to get very far” (Daniel Goleman).

“True compassion means not only feeling another’s pain but also being moved to help relieve it” (Daniel Goleman).

More quotes from Daniel Goleman

Follow Daniel Goleman on social media:

 

Keywords: EI; Emotional Intelligence; Self control; Empathy; Alexithymia.

References

Goleman, D. (1998). Working with emotional intelligence. Bantam.

From Bedside to Classroom: The Nurse Educator Transition Model

Joural Club Article

Schoening, A. (2013). From Bedside to Classroom: The Nurse Educator Transition Model. Nursing Education Perspectives, 34(3), 167-72.

Aims

Qualitative study with the aim to generate a theoretical model describing the social process during the transition of nurse to nurse educator.

Methods

Qualitative study utilising grounded theory involving 20 nurse educators from the United States of America. The nurse educators were from different specialities of practice and from a mix of public and private health.

Background

The difficulty transitioning from nurse to nurse educator and then into the academic setting. The lack of preparation for teaching and the skills required. Moving into the academic world provides ‘a reality shock’ as the nurse adapts to the new role. The academic environment is seen as unfamiliar, a lack of guidance and orientation.

Results

4 phases were identified as integral to the transition from nurse to nurse educator, to form the Nurse Educator Transition (NET) model:

  1. Anticipation/expectation.
  2. Disorientation.
  3. Information seeking.
  4. Identity formation.

A teaching assistanship to gain experience, support and feedback was highlighted as desirable opportunities. Often employers expected nurse educators to understand pedalogical and curricular knowledge, and did little to prepare educators for the experience ahead.

Recommendations

  • Formal pedalogical training.
  • Competency based orientation programs.
  • Mentoring should be provided, both formal and informal (peer to peer).

The themes identified regarding nurse educator transition are similar to those of the new nurse and transition from novice to expert.

Reference

Oermann, M. H. (Ed.). (2013). Teaching in nursing and role of the educator: The complete guide to best practice in teaching, evaluation and curriculum development. Springer Publishing Company. [sample]

Penn, B. K., Wilson, L. D., & Rosseter, R. (2008). Transitioning from nursing practice to a teaching roleOnline Journal of Issues in Nursing13(3).

Ross, P. (2016) A Guide for the New Nurse Educator. Nursing Education Network.

Schoening, A. (2013). From Bedside to Classroom: The Nurse Educator Transition Model. Nursing Education Perspectives, 34(3), 167-72.

 

 

 

 

 

 

https://pdfs.semanticscholar.org/bf23/47d8897cdb170e7772786ff844c70da0806f.pdf

Big Data & Machine Learning

Journal Club Article: Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicineThe New England Journal of Medicine375(13), 1216.

Background

  • “Big data will transform medicine. It’s essential to remember, however, that data by themselves are useless. To be useful, data must be analyzed, interpreted, and acted on. Thus it is algorithms — not data sets — that will prove transformative.
  • Machine learning, conversely, approaches problems as a doctor progressing through residency might: by learning rules from data. Starting with patient-level observations, algorithms sift through vast numbers of variables, looking for combinations that reliably predict outcomes.
  • But where machine learning shines is in handling enormous numbers of predictors — sometimes, remarkably, more predictors than observations — and combining them in nonlinear and highly interactive ways.
  • Consider a chest radiograph. Some radiographic features might predict an important outcome, such as death. In a standard statistical model, we might use the radiograph’s interpretation — “normal,” “atelectasis,” “effusion” — as a variable. But instead, why not let the data speak for themselves?

Precautionary Aspects

  • Of course, letting the data speak for themselves can be problematic. Algorithms might “overfit” predictions to spurious correlations in the data.
  • Correlated predictors could produce unstable estimates.
  • Another key issue is the quantity and quality of input data. Machine learning algorithms are highly “data hungry,” often requiring millions of observations to reach acceptable performance levels.
  • In addition, biases in data collection can substantially affect both performance and generalizability.”

Changes to Healthcare

Obermeyer & Emanuel (2016) state that in the future the ability to transform data into knowledge will be disrupted in at least three key areas due to machine learning:

  1. Improved prognostic’s.
  2. Machine accuracy will displace much of the work of radiologists and anatomical pathologists through machine learning.
  3. Improved diagnostic accuracy.

Despite concerns that with robotics and artificial intelligence the ability of machine learning may actually improve healthcare delivery. “Machine learning will become an indispensable tool for clinicians seeking to truly understand their patients. As patients’ conditions and medical technologies become more complex, its role will continue to grow, and clinical medicine will be challenged to grow with it”.

Keywords: Big data; machine learning; disruption; analytics; artificial intelligence.

References

High, R. (2012). The era of cognitive systems: An inside look at IBM Watson and how it worksIBM Corporation, Redbooks.

IBM (2017). Cognitive Analytics and 6 Solutions: IBM Research – Haifa.

Marr, B. (2016). How Machine Learning, Big Data And AI Are Changing Healthcare Forever. Forbes.com.

Nursing Education Network (2017). Analytics & Big Data.

Nursing Education Network (2017). Educating Nurses for the Future of Healthcare.

Nursing Education Network (2017). Industry 4.0: The Future of Work.

Obermeyer, Z. & Emanuel, E. J. (2016). Predicting the Future — Big Data, Machine Learning, and Clinical Medicine. NEJM Catalyst.

 

 

 

Deimplementation of Practice

Journal Club Articles: 

Niven, D. J., Mrklas, K. J., Holodinsky, J. K., Straus, S. E., Hemmelgarn, B. R., Jeffs, L. P., & Stelfox, H. T. (2015). Towards understanding the de-adoption of low-value clinical practices: a scoping reviewBMC medicine13(1), 255.

Niven, D. J., Rubenfeld, G. D., Kramer, A. A., & Stelfox, H. T. (2015). Effect of published scientific evidence on glycemic control in adult intensive care unitsJAMA internal medicine175(5), 801-809.

Background

Evidence Based Practice (EBP) is the cornerstone for nursing practice, to ensure quality care and interventions are initiated based on research and not ritualistic practices.

Dissemination of Practice: The translation of practice from research into the clinical domain is known to take a long time. 17 years is the time lag (Morris et al., 2011).

Now if it takes 17 years to translate research into practice how long does it take healthcare to deimplementate practice that may be out of date and lacking in evidence?

Deimplementation of Practice

The process of the deadoption of healthcare practices with evidence of ineffectiveness or harm.

An excellent example by Niven et al., (2015) large multi-centre database time-series analysis to determine whether tight glycaemic control (supporting adoption) and NICE-SUGAR trial (supporting deadoption) was influencing the practice of glycaemic control in adult ICU’s.

Niven et al., (2015) conclusion form the review of deadoption on tight glycaemic control states “there was a slow steady adoption of tight glycaemic control following publication of a clinical trial that suggested benefit, with little to no deadoption following a subsequent trial that demonstrated harm. There is an urgent need to understand and promote deadoption of ineffective clinical practices.”

Practice Considerations

Take a few minutes to reflect on practice in your clinical setting and if your unit has guidelines that support old evidence, traditional and not supported with evidence based grounding. This is where the local clinical audit could help benchmark to current evidence and aid the change process to update practice. Happy PDSA cycling (plan-do-study-act).

Keywords: Deadoption; Deimplementation; EBP; ineffective practice.

References

Courtney, M. D. & McCutcheon, H. (2009). Using evidence to guide nursing practice. Elsevier Australia.

Melnyk, B. M., & Fineout-Overholt, E. (Eds.). (2011). Evidence-based practice in nursing & healthcare: A guide to best practice. Lippincott Williams & Wilkins.

Morris, Z. S., Wooding, S., & Grant, J. (2011). The answer is 17 years, what is the question: understanding time lags in translational researchJournal of the Royal Society of Medicine104(12), 510-520.

Niven, D. J., Mrklas, K. J., Holodinsky, J. K., Straus, S. E., Hemmelgarn, B. R., Jeffs, L. P., & Stelfox, H. T. (2015). Towards understanding the de-adoption of low-value clinical practices: a scoping reviewBMC medicine13(1), 255.

Niven, D. J., Rubenfeld, G. D., Kramer, A. A., & Stelfox, H. T. (2015). Effect of published scientific evidence on glycemic control in adult intensive care unitsJAMA internal medicine175(5), 801-809.

Polit, D. F., & Beck, C. T. (2008). Nursing research: Generating and assessing evidence for nursing practice. Lippincott Williams & Wilkins.

Sackett, D. L. (1997). Evidence-based Medicine How to practice and teach EBM. WB Saunders Company.

Book Club: The Great Brain Race: How Global Universities are Reshaping the World.

Book: Wildavsky, B. (2012). The great brain race: How global universities are reshaping the world. Princeton University Press. [Chapter 1]

The internationalision of student mobility and how “every year, nearly three million international students study outside of their home countries, a 40 percent increase since 1999”. Higher education and research is a global activity and is now considered border less.

University Challenge

How the top Universities in developing countries are partners with the top ‘traditional’ old world universities from the developed world. The resources, campus, teachers of these new competitors are providing world class facilities to their student population. What they lack in history, they provide an innovative and technological driven education.

World Rankings

The now border less world of higher education and the importance of world rankings of universities to attract both students and teachers in the ‘global supermarket‘ of education.

The Brain Drain

With this worldwide expansion of higher education and a mobile student group, the effect has been a “brain drain”. The best students have been taken from their home countries to assist in the brain gain and growth in the land of their university where they then undertake their working career. Foreign students bring both academic and economic competitiveness. They boost this economy but their home economy looses out, until they return home.

With the new universities challenging the traditional and also free world trade, it is possible that there will be a change to the brain drain to create an equilibrium of brain circulation.

Image by jesse orrico

Reference: 

Wildavsky, B. (2012). The great brain race: How global universities are reshaping the world. Princeton University Press. [Chapter 1]

 

John Heron’s Six‐Category Intervention Analysis

Resource: Heron, J. (2001). Helping the client: A creative practical guide. Sage.

Purpose

Heron’s Six‐Category Intervention Analysis is a conceptual framework for understanding interpersonal relationships.

What Is Intervention?

Heron’s meaning of intervention is an “identifiable piece of verbal or non- verbal behaviour that is part of the practitioner’s service to the client”.

“The practitioner is anyone who offers a professional service to the client. This would include such disciplines as nurses, doctors, dentists, psychiatrists, psychologists, counsellors” (Rungapadiachy, 1998).

Heron’s Six‐Category Intervention Analysis

Enhancement of growth and development is seen as therapeutic activity. Heron’s 6 categories of counselling interventions are based around 2 styles of authoritative and facilitative:

Authoritative:   
  • Prescriptive: Seeks to direct the client’s behaviour.
  • Informative: Seeks to impart knowledge to the client.
  • Confronting: Seeks to raise the client’s awareness or consciousness about attitudes or behaviours of which they are unaware of.
Facilitative:
  • Cathartic: Seeks to enable the client to discharge painful emotions.
  • Catalytic: Seeks to encourage the client into self-discovery, self-directing and problem-solving approach.
  • Supportive: Seeks to affirm the client’s worth and value and understand their qualities, attitudes and actions.
Purpose
  • to understand how we are interacting with people.
  • to understand how we are received.
  • to understand ourselves.
  • to understand our client.

Tools

Keywords: Heron; Six‐Category Intervention Analysis; Interpersonal Relationships.

References

Heron, J. (2001). Helping the client: A creative practical guide. Sage.

Rungapadiachy, D. M. (1998). Interpersonal communication and psychology for health care professionals: Theory and practice. Elsevier Health Sciences.

Sloan, G., & Watson, H. (2001). John Heron’s six‐category intervention analysis: towards understanding interpersonal relations and progressing the delivery of clinical supervision for mental health nursing in the United KingdomJournal of advanced nursing36(2), 206-214.