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Healthcare Simulation Standards of Best PracticeTM Outcomes and Objectives

      Abstract

      KEYWORDS

      As the science of simulation continues to evolve, so does the need for additions and revisions to the Healthcare Simulation Standards of Best Practice. Therefore, the Healthcare Simulation Standards of Best PracticeTM are living documents.

      Standard

      All simulation-based experiences (SBE) originate with the development of measurable objectives designed to achieve expected behaviors and outcomes. A SBE is defined as “An array of structured activities that represent actual or potential situations in education and practice. These activities allow learners to develop or enhance their knowledge, skills, and attitudes, or to analyze and respond to realistic situations in a simulated environment”.
      • Pilcher J.
      • Heather G.
      • Jensen C.
      • Huwe V.
      • Jewell C.
      • Reynolds R.
      • Karlsen K.A.
      Simulation-based learning: It's not just for NRP.
      Current literature demonstrates the use of simulation in educational settings, to facilitate achievement of cognitive, psychomotor, and affective skills.
      • In Lioce L.
      • Lopreiato J.
      • Downing D.
      • Chang T.P.
      • Robertson J.M.
      • Anderson M.
      • Diaz D.A.
      • Spain A.E.
      Healthcare simulation dictionary-second edition.

      Background

      The development of the simulation-based experience (SBE) originates after an educational need has been identified. The needs assessment informs the learning objectives. The SBE is constructed through the development of objectives, as guided by the identified outcomes. Outcomes are influenced by the accrediting bodies, program, clinical agency, course, or patient care needs. For learners to achieve intended objectives and/or outcomes, simulationists need to create or use valid and reliable scenarios.
      • Cantrell M.A.
      • Franklin A.
      • Leighton K.
      • Carlson A.
      The evidence in simulation-based learning experiences in nursing education and practice: An umbrella review.
      ,
      • Mirza N.
      • Cinel J.
      • Noyes H.
      • McKenzie W.
      • Burgess K.
      • Blackstock S.
      • Sanderson D.
      Simulated patient scenario development: A methodological review of validity and reliability reporting.
      ,
      • Hoggan C.D.
      Transformative learning as a metatheory: Definition, criteria, and typology.
      • Billings D.
      • Halstead J.
      Teaching in nursing: A guide for faculty.
      INACSL Standards Committee (2016, December). INACSL standards of best practice: SimulationSM Participant evaluation.

      Outcomes

      Considered essential to learning, outcomes are a “measurable result of the learners progress toward meeting a set of objectives.”
      INACSL Standards Committee
      INACSL standards of best practice: SimulationSM Simulation glossary.
      An integral component of instructional and research design, outcomes are used by simulationist, clinicians, and researchers to determine the impact of simulation-based experiences.
      The INASCL Board of Directors
      Standard I: Terminology.
      Expected outcomes are the change in knowledge, skills, and/or attitudes as a result of the SBE.
      • Hoggan C.D.
      Transformative learning as a metatheory: Definition, criteria, and typology.
      • Billings D.
      • Halstead J.
      Teaching in nursing: A guide for faculty.
      The New World Kirkpatrick Model
      • Kirkpatrick J.D.
      • Kirkpatrick W.K.
      Kirkpatrick's four levels of training evaluation.
      provides four sequential levels of evaluation: (1) Reaction –measures the degree to which learners find the training favorable, engaging, and relevant to their jobs; (2) Learning - measures the degree to which learners acquire the intended knowledge, skills, attitude, confidence and commitment based on their participation in the training; (3) Behavior - measures the degree to which learners apply what they learned during training when they are back on the job; and (4) Results - measures the degree to which targeted outcomes occur as a result of the training, support, and accountability.

      Objectives

      Once SBE outcomes have been determined, the next step is to develop objectives. Objectives are the blueprint for simulation design.
      • MacLean S.
      • Geddes F.
      • Kelly M.
      • Della P.
      Realism and presence in simulation: Nursing student perceptions and learning outcomes.
      Objectives are guiding tools to facilitate achievement of simulation-based learning outcomes and the hallmark of sound educational design.
      • MacLean S.
      • Geddes F.
      • Kelly M.
      • Della P.
      Realism and presence in simulation: Nursing student perceptions and learning outcomes.
      Defined as “statements of specific measurable results that learners are expected to achieve during SBE”, written objectives may encompass cognitive (knowledge), skills (psychomotor), and affective (attitude) domains of learning that advance the learners' level of knowledge, skills, and experience.
      INACSL Standards Committee
      INACSL standards of best practice: SimulationSM Simulation glossary.
      All objectives should be created to facilitate transfer of knowledge to prepare for practice of safe patient care.
      • Pilcher J.
      • Heather G.
      • Jensen C.
      • Huwe V.
      • Jewell C.
      • Reynolds R.
      • Karlsen K.A.
      Simulation-based learning: It's not just for NRP.
      • In Lioce L.
      • Lopreiato J.
      • Downing D.
      • Chang T.P.
      • Robertson J.M.
      • Anderson M.
      • Diaz D.A.
      • Spain A.E.
      Healthcare simulation dictionary-second edition.
      Learning objectives also assist in determining what type of simulation tool/model/manikin and fidelity should be utilized. Choosing a simulation tool, model, or manikin with appropriate modality or characteristics to enable the achievement of learning objectives is salient to the design process.
      • McDermott D.S.
      • Sarasnick J.
      • Timcheck P.
      Using the INACSL simulation design standard for novice participants.
      • Choi W.
      • Dyens O.
      • Chan T.
      • Schijven M.
      • Lajoie S.
      • Mancini M.E.
      • Lau J.
      Engagement and learning in simulation: recommendations of the Simnovate Engaged Learning Domain Group.
      • Foronda C.L.
      • Fernandez-Burgos M.
      • Kelley C.N.
      • Henry M.N.
      Virtual simulation in nursing education: A systematic review spanning 1996-2018.
      Objectives created for the SBE should be articulated and goal-directed to achieve the desired outcome. To maintain psychological safety, simulationists should disclose essential information and objectives with the learners before engaging in a formative or summative SBE.
      • Rourke S.
      How does virtual reality simulation compare to simulated practice in the acquisition of clinical psychomotor skills for pre-registration student nurses? A systematic review.
      • Palaganas J.
      • Maxworthy J.
      • Epps C.
      • Mancini M.
      Defining excellence in simulation programs.
      INACSL Standards Committee (2016, December). INACSL standards of best practice: SimulationSM Facilitation..
      In general, this will include broad information and context, but may not include critical actions before the initiation of the simulation activity. In addition, learning objectives must consider the needs of the learner. Moreover, during simulation design, learning objectives are developed in alignment with Blooms’ Revised Taxonomy
      • Adams N.E.
      Blooms Taxonomy of cognitive learning objectives.
      • Barari N.
      • RezaeiZadeh M.
      • Khorasani A.
      • Alami F.
      Designing and validating educational standards for E-teaching in virtual learning environments (VLEs), based on revised Bloom's taxonomy.
      • Hanshaw S.L.
      • Dickerson S.S.
      High fidelity simulation evaluation studies in nursing education: A review of the literature.
      • Bloom B.
      Taxonomy of educational objectives: The classification of educational goals. Handbook 1: Cognitive domain.
      Blooms’ Revised Taxonomy
      • Hanshaw S.L.
      • Dickerson S.S.
      High fidelity simulation evaluation studies in nursing education: A review of the literature.
      • Bloom B.
      Taxonomy of educational objectives: The classification of educational goals. Handbook 1: Cognitive domain.
      provides a framework for developing and leveling objectives to meet expected outcomes. The taxonomy classifies three domains of learning: cognitive (knowledge), psychomotor (skills), and affective (attitudes)
      • Adams N.E.
      Blooms Taxonomy of cognitive learning objectives.
      • Barari N.
      • RezaeiZadeh M.
      • Khorasani A.
      • Alami F.
      Designing and validating educational standards for E-teaching in virtual learning environments (VLEs), based on revised Bloom's taxonomy.
      • Hanshaw S.L.
      • Dickerson S.S.
      High fidelity simulation evaluation studies in nursing education: A review of the literature.
      • Bloom B.
      Taxonomy of educational objectives: The classification of educational goals. Handbook 1: Cognitive domain.
      . Each learning domain has a hierarchical taxonomy applicable to simulation activities. The revised Blooms’ Taxonomy 19 hierarchy progresses from the lower level objectives, remembering and understanding, to the higher-level objectives, applying, analyzing, evaluating, and creating. These action verbs provide structure and communicate the knowledge, skills, and attitudes (KSA) the learner is intended to achieve as a result of involvement in a simulation-based activity.
      • Adams N.E.
      Blooms Taxonomy of cognitive learning objectives.
      • Barari N.
      • RezaeiZadeh M.
      • Khorasani A.
      • Alami F.
      Designing and validating educational standards for E-teaching in virtual learning environments (VLEs), based on revised Bloom's taxonomy.
      • Hanshaw S.L.
      • Dickerson S.S.
      High fidelity simulation evaluation studies in nursing education: A review of the literature.
      • Bloom B.
      Taxonomy of educational objectives: The classification of educational goals. Handbook 1: Cognitive domain.
      Furthermore, when creating learning objectives, scaffolding SBE objectives requires the simulationist to guide the learner to apply their knowledge and skills by building upon foundational knowledge.
      • Bjerke M.B.
      • Renger R.
      Being smart about writing SMART objectives.
      • Chatterjee D.
      • Corral J.
      How to write well-defined learning objectives.
      By doing so, the overall cognitive load imposed during the SBE can be reduced and therefore improve integration of new knowledge.
      • Herrington A.
      • Schneidereith T.
      Scaffolding and sequencing core concepts to develop a simulation-integrated nursing curriculum.
      • Seufert T.
      The interplay between self-regulation in learning and cognitive load.
      • Josephsen J.
      Cognitive load theory and nursing simulation: An integrative review.
      Alignment of cognitive load with learner's readiness promotes improved expertise development and problem-solving during SBE.
      • Herrington A.
      • Schneidereith T.
      Scaffolding and sequencing core concepts to develop a simulation-integrated nursing curriculum.
      • Seufert T.
      The interplay between self-regulation in learning and cognitive load.
      • Josephsen J.
      Cognitive load theory and nursing simulation: An integrative review.
      • Sweller J.
      Cognitive load during problem solving: Effects on learning.
      Moreover, learning depends on sufficient room in memory stores to process new information.
      • Sun N.Z.
      • Anand P.A.
      • Snell L.
      Optimizing the design of high-fidelity simulation-based training activities using cognitive load theory – lessons learned from a real-life experience.
      • Fraser K.L.
      • Meguerdichian M.J.
      • Haws J.T.
      • Grant V.J.
      • Bajaj K.
      • Cheng A.
      Cognitive load theory for debriefing simulations: implications for faculty development.
      Vygotsky's theory of the zone of proximal development encourages effective learning by advancing the learner through the learning process step-by-step until they can conduct themselves without assistance.

      David L. (2014). Social development theory (Vygotsky): Learning Theories. Retrieved from: https://www.learning-theories.com/vygotskys-social-learning-theory.html.

      This zone of proximal learning allows the learner to safely advance while building on prior knowledge.
      In order to have achievable outcomes, clearly defined, measurable objectives are necessary. In the field of corporate management, Doran
      • Doran G.
      There's a S.M.A.R.T. way to write management's goals and objectives.
      created the acronym S.M.A.R.T. (specific, measurable, assignable, realistic, and time related) as a framework to develop meaningful, measurable objectives. Organizations have adapted the principles with differing, yet similar criteria.
      • Bjerke M.B.
      • Renger R.
      Being smart about writing SMART objectives.
      The S.M.A.R.T framework is used to write and contextualize desired KSAs that simulation learners should demonstrate upon completion of SBE
      • Bjerke M.B.
      • Renger R.
      Being smart about writing SMART objectives.
      ,
      • Doran G.
      There's a S.M.A.R.T. way to write management's goals and objectives.
      ,
      • Lawlor K.
      • Hornyak M.
      SMART goals: How the application of SMART goals can contribute to achievement of student learning outcomes.
      The Center for Disease Control
      • Abuaiadah D.
      • Burrell C.
      • Bosu M.
      • et al.
      Assessing learning outcomes of course descriptors containing object-oriented programming concepts.
      provides academia and the healthcare industry with the following S.M.A.R.T. criteria for writing objectives:
      • Bloom B.
      Taxonomy of educational objectives: The classification of educational goals. Handbook 1: Cognitive domain.
      • Bjerke M.B.
      • Renger R.
      Being smart about writing SMART objectives.
      ,

      David L. (2014). Social development theory (Vygotsky): Learning Theories. Retrieved from: https://www.learning-theories.com/vygotskys-social-learning-theory.html.

      • Doran G.
      There's a S.M.A.R.T. way to write management's goals and objectives.
      • Lawlor K.
      • Hornyak M.
      SMART goals: How the application of SMART goals can contribute to achievement of student learning outcomes.
      • Specific: What exactly are we going to do for whom? Is the objective clearly worded using strong action verbs? Are terms concrete, well-defined, and learners informed of what is expected?
      • Measurable: Is it quantifiable and measurable? Consider numbers and units of measure for comparison.
      • Achievable: Can the SBE be completed in the proposed time frame with the resources and support available? What are the limitations to consider?
      • Realistic: Will the SBE have an effect on the desired goal or outcome? Are the resources required available for this activity?
      • Time phased: When will this objective be accomplished? What is the stated timeline?
      Potential consequences of not following S.M.A.R.T. criteria within this standard can lead to ambiguity, unintended outcomes, and failure to meet objectives of the SBE.
      • Bloom B.
      Taxonomy of educational objectives: The classification of educational goals. Handbook 1: Cognitive domain.
      ,
      • Doran G.
      There's a S.M.A.R.T. way to write management's goals and objectives.
      ,
      • Abuaiadah D.
      • Burrell C.
      • Bosu M.
      • et al.
      Assessing learning outcomes of course descriptors containing object-oriented programming concepts.
      This may include skewed evaluation results; decreased learner satisfaction; failure to achieve desired KSA's; and/or lack of change in quality and safety indicators.

      Criteria necessary to meet this standard:

      Criterion 1: Establish learner outcomes influenced by accreditation, program, curriculum and/or patient care needs that are measurable and appropriately scaffolded to learner knowledge, skills, and attitudes. 3,6-9,11,23-26,34
      Required Elements for Outcomes are:
      • Consistent with the mission & vision of the program.
      • Based on programmatic goals.
      • Based upon needs assessment, evidence-based practice, clinical partners, and stakeholders.
      • Representative of equity, inclusivity & diversity.
      • Consistent with an identified framework i.e. New World Kirkpatrick's Model (reaction, learning, behavior, & results).
      • Aligned with Healthcare Simulation Standards of Best PracticeTM (HSSOBPTM) Simulation Design (Follow the HSSOBPTM Simulation Design).
      • Driven by objectives within educational or clinical setting.
      • Communicated purposefully to learners in advance of SBE.
      Criterion 2: Create objectives for the simulation-based experience to meet defined outcome based on formative or summative evaluation.
      • Mirza N.
      • Cinel J.
      • Noyes H.
      • McKenzie W.
      • Burgess K.
      • Blackstock S.
      • Sanderson D.
      Simulated patient scenario development: A methodological review of validity and reliability reporting.
      ,
      • Billings D.
      • Halstead J.
      Teaching in nursing: A guide for faculty.
      ,
      • Kirkpatrick J.D.
      • Kirkpatrick W.K.
      Kirkpatrick's four levels of training evaluation.
      • MacLean S.
      • Geddes F.
      • Kelly M.
      • Della P.
      Realism and presence in simulation: Nursing student perceptions and learning outcomes.
      • McDermott D.S.
      • Sarasnick J.
      • Timcheck P.
      Using the INACSL simulation design standard for novice participants.
      ,
      • Palaganas J.
      • Maxworthy J.
      • Epps C.
      • Mancini M.
      Defining excellence in simulation programs.
      ,
      INACSL Standards Committee (2016, December). INACSL standards of best practice: SimulationSM Facilitation..
      ,
      • Doran G.
      There's a S.M.A.R.T. way to write management's goals and objectives.
      ,
      • Abuaiadah D.
      • Burrell C.
      • Bosu M.
      • et al.
      Assessing learning outcomes of course descriptors containing object-oriented programming concepts.
      Required Elements for Objectives are:
      • Goal-driven.
      • Scaffolded appropriately, incorporating level of attainment based upon the revised Bloom's Taxonomy model. Remembering being at the lowest level, understanding, applying, and analyzing in the middle, and evaluating and creating being the highest.
      • Reflective of S.M.A.R.T. strategies.
      • Aligned by mapping with outcomes established for the course, program, institution and/or accrediting body.
      Criterion 3: Identify appropriate simulation modality to meet the learning objectives/outcomes.
      • McDermott D.S.
      • Sarasnick J.
      • Timcheck P.
      Using the INACSL simulation design standard for novice participants.
      • Choi W.
      • Dyens O.
      • Chan T.
      • Schijven M.
      • Lajoie S.
      • Mancini M.E.
      • Lau J.
      Engagement and learning in simulation: recommendations of the Simnovate Engaged Learning Domain Group.
      • Foronda C.L.
      • Fernandez-Burgos M.
      • Kelley C.N.
      • Henry M.N.
      Virtual simulation in nursing education: A systematic review spanning 1996-2018.
      Examples of simulation modality are:
      • Low technology (i.e. task trainer, case study, role play).
      • High technology (i.e. high complexity simulation manikin mimicking human body functions).
      • Simulated Patient (i.e. live patient versus virtual patient technology).
      • Virtual/Augmented Simulation (i.e. three-dimensional (3D) immersion using Head-mounted Display VR (HMD VR), haptic enhanced task trainers, computer screen-based, immersive rooms, interactive clinical case scenarios with branching case structure).
      Criterion 4: Identify appropriate fidelity to meet the learning objectives/outcomes. 12-14,34-38
      Examples of fidelity are:
      • Conceptual (i.e. vital signs and lab results reflect the diagnosis).
      • Physical/Environmental (i.e. setting of in-situ versus simulation lab, equipment, tools, sensory props, manikin, moulage).
      • Psychological (i.e. evokes underlying emotions, beliefs, and self-awareness of learners).
      Criterion 5: Establish guidelines for facilitation of SBE to meet objectives.
      • Rourke S.
      How does virtual reality simulation compare to simulated practice in the acquisition of clinical psychomotor skills for pre-registration student nurses? A systematic review.
      • Palaganas J.
      • Maxworthy J.
      • Epps C.
      • Mancini M.
      Defining excellence in simulation programs.
      INACSL Standards Committee (2016, December). INACSL standards of best practice: SimulationSM Facilitation..
      • Adams N.E.
      Blooms Taxonomy of cognitive learning objectives.
      • Barari N.
      • RezaeiZadeh M.
      • Khorasani A.
      • Alami F.
      Designing and validating educational standards for E-teaching in virtual learning environments (VLEs), based on revised Bloom's taxonomy.
      • Hanshaw S.L.
      • Dickerson S.S.
      High fidelity simulation evaluation studies in nursing education: A review of the literature.
      Required Elements:
      • Aligned with Healthcare Simulation Standards of Best PracticeTM. (Follow the HSSOBPTM Simulation Design).
        INACSL Standards Committee
        INACSL standards of best practice: SimulationSM simulation design.
      • Fundamental guides for teaching or evaluation.
      • A clear understanding of expectations for the SBE learners.
      • Simulationists that are trained and deemed competent in facilitation of simulation-based experiences, as described in the HSSOBPTM Professional Development.

      Original INACSL Standard

      The INACSL Board of Directors (2011, August). Standard III: Participant objectives. Clinical Simulation in Nursing, 7(4S), s10-s11.doi:10.1016/j.ecns.2011.05.007

      Subsequent Standard

      Lioce, L., Reed, C. C., Lemon, D., King, M. A., Martinez, P. A., Franklin, A. E., Boese, T., Decker, S., Sando, C. R., Gloe, D., Meakim, C., & Borum, J. C. (2013, June). Standards of Best Practice: Simulation Standard III: Participant Objectives. Clinical Simulation in Nursing, 9(6S), S15-S18. http://dx.doi.org/10.1016/j.ecns.2013.04.005.
      INACSL Standards Committee (2016, December). INACSL Standards of Best Practice: SimulationSM Outcomes and objectives. Clinical Simulation in Nursing, 12(S), S13-S15. http://dx.doi.org/10.1016/j.ecns.2016.09.006

      About the International Nursing Association for Clinical Simulation and Learning (INACSL)

      The International Nursing Association for Clinical Simulation and Learning (INACSL) is the global leader in transforming practice to improve patient safety through excellence in health care simulation. INACSL is a community of practice for simulation where members can network with simulation leaders, educators, researchers, and industry partners. INACSL also provided the original living documents INACSL Standards of Best Practice: SimulationSM, an evidence-based framework to guide simulation design, implementation, debriefing, evaluation, and research. The Healthcare Simulation Standards of Best PracticeTM are provided with the support and input of the international community and sponsored by INACSL.

      References

        • Pilcher J.
        • Heather G.
        • Jensen C.
        • Huwe V.
        • Jewell C.
        • Reynolds R.
        • Karlsen K.A.
        Simulation-based learning: It's not just for NRP.
        Neonatal Network. 2012; 31: 281-288
        • In Lioce L.
        • Lopreiato J.
        • Downing D.
        • Chang T.P.
        • Robertson J.M.
        • Anderson M.
        • Diaz D.A.
        • Spain A.E.
        Healthcare simulation dictionary-second edition.
        Agency for Healthcare Research and Quality, Rockville, MD2020
        • Cantrell M.A.
        • Franklin A.
        • Leighton K.
        • Carlson A.
        The evidence in simulation-based learning experiences in nursing education and practice: An umbrella review.
        Clinical Simulation in Nursing. 2017; 13 (http://dx.doi.org/): 634-667https://doi.org/10.1016/j.ecns.2017.08.004
        • Mirza N.
        • Cinel J.
        • Noyes H.
        • McKenzie W.
        • Burgess K.
        • Blackstock S.
        • Sanderson D.
        Simulated patient scenario development: A methodological review of validity and reliability reporting.
        Nurse Education Today. 2020; 85 (Doi: https://doi.org/)104222https://doi.org/10.1016/j.nedt.2019.104222
        • INACSL Standards Committee
        INACSL standards of best practice: SimulationSM Simulation glossary.
        Clinical Simulation in Nursing. 2016; 12 (http://dx.doi.org/): S39-S47https://doi.org/10.1016/j.ecns.2016.09.012
        • The INASCL Board of Directors
        Standard I: Terminology.
        Clinical Simulation in Nursing. 2011; 7 (http://dx.doi.org/): s3-s7https://doi.org/10.1016/j.ecns.2011.05.005
        • Hoggan C.D.
        Transformative learning as a metatheory: Definition, criteria, and typology.
        Adult Education Quarterly. 2016; 66: 57-75https://doi.org/10.1177/0741713615611216
        • Billings D.
        • Halstead J.
        Teaching in nursing: A guide for faculty.
        6th edition. Elsevier, St. Louis2020
      1. INACSL Standards Committee (2016, December). INACSL standards of best practice: SimulationSM Participant evaluation.
        Clinical Simulation in Nursing. 2016; 12 (http://dx.doi.org/): S26-S29https://doi.org/10.1016/j.ecns.2016.09.009
        • Kirkpatrick J.D.
        • Kirkpatrick W.K.
        Kirkpatrick's four levels of training evaluation.
        Association for Talent Development, 2016
        • MacLean S.
        • Geddes F.
        • Kelly M.
        • Della P.
        Realism and presence in simulation: Nursing student perceptions and learning outcomes.
        Journal of Nursing Education. 2019; 58 (Doi:): 330-338https://doi.org/10.3928/01484834-20190521-03
        • McDermott D.S.
        • Sarasnick J.
        • Timcheck P.
        Using the INACSL simulation design standard for novice participants.
        Clinical Simulation in Nursing. 2017; 13 (http://dx.doi.org/): 249-253https://doi.org/10.1016/j.ecns.2017.03.003
        • Choi W.
        • Dyens O.
        • Chan T.
        • Schijven M.
        • Lajoie S.
        • Mancini M.E.
        • Lau J.
        Engagement and learning in simulation: recommendations of the Simnovate Engaged Learning Domain Group.
        BMJ Simulation and Technology Enhanced Learning. 2017; 3 (Supplhttp://dx.doi.org/): S23-S32https://doi.org/10.1136/bmjstel-2016-000177
        • Foronda C.L.
        • Fernandez-Burgos M.
        • Kelley C.N.
        • Henry M.N.
        Virtual simulation in nursing education: A systematic review spanning 1996-2018.
        Simulation in Healthcare. 2020; 15 (https://doi:): 46-54https://doi.org/10.1097/SIH.0000000000000411
        • Rourke S.
        How does virtual reality simulation compare to simulated practice in the acquisition of clinical psychomotor skills for pre-registration student nurses? A systematic review.
        International Journal of Nursing Studies. 2020; 102: 1-7https://doi.org/10.1016/j.ijnurstu.2019.103466
        • Palaganas J.
        • Maxworthy J.
        • Epps C.
        • Mancini M.
        Defining excellence in simulation programs.
        Society for Simulation in Healthcare. Wolters Kluwer, Philadelphia, PA2015
      2. INACSL Standards Committee (2016, December). INACSL standards of best practice: SimulationSM Facilitation..
        Clinical Simulation in Nursing. 2016; 12 (http://dx.doi.org/): S16-S20https://doi.org/10.1016/j.ecns.2016.09.007
        • Adams N.E.
        Blooms Taxonomy of cognitive learning objectives.
        Journal of the Medical Library Association. 2015; 103 (http://dx.doi.org/): 152-153https://doi.org/10.3163/1536-5050.103.3.010
        • Barari N.
        • RezaeiZadeh M.
        • Khorasani A.
        • Alami F.
        Designing and validating educational standards for E-teaching in virtual learning environments (VLEs), based on revised Bloom's taxonomy.
        Interactive Learning Environments. 2020; 1-13 (Doi:)https://doi.org/10.1080/10494820.2020.1739078
        • Hanshaw S.L.
        • Dickerson S.S.
        High fidelity simulation evaluation studies in nursing education: A review of the literature.
        Nurse Education in Practice. 2020; (Doi.org/)102818https://doi.org/10.1016/j.nepr.2020.102818
        • Bloom B.
        Taxonomy of educational objectives: The classification of educational goals. Handbook 1: Cognitive domain.
        Longman, 1956
        • Bjerke M.B.
        • Renger R.
        Being smart about writing SMART objectives.
        Evaluation and Program Planning. 2017; 61 (Doi: http://dx.doi.org/): 125-127https://doi.org/10.1016/j.evalprogplan.2016.12.009
        • Chatterjee D.
        • Corral J.
        How to write well-defined learning objectives.
        The Journal of Education in Perioperative Medicine: JEPM. 2017; 19
        • Herrington A.
        • Schneidereith T.
        Scaffolding and sequencing core concepts to develop a simulation-integrated nursing curriculum.
        Nurse Educator. 2017; 42: 204-207https://doi.org/10.1097/NNE.0000000000000358
        • Seufert T.
        The interplay between self-regulation in learning and cognitive load.
        Educational Research Review. 2018; 24 (http://doi.org./): 116-129https://doi.org/10.1016/j.edurev.2018.03.004
        • Josephsen J.
        Cognitive load theory and nursing simulation: An integrative review.
        Clinical Simulation in Nursing. 2016; 11 (http://dx.doi.org/): 259-267https://doi.org/10.1016/j.ecns.2015.02.004
        • Sweller J.
        Cognitive load during problem solving: Effects on learning.
        Cognitive Science. 1988; 12: 257-285
        • Sun N.Z.
        • Anand P.A.
        • Snell L.
        Optimizing the design of high-fidelity simulation-based training activities using cognitive load theory – lessons learned from a real-life experience.
        Journal of Simulation. 2017; 11: 151-158https://doi.org/10.1057/s41273-016-0001-5
        • Fraser K.L.
        • Meguerdichian M.J.
        • Haws J.T.
        • Grant V.J.
        • Bajaj K.
        • Cheng A.
        Cognitive load theory for debriefing simulations: implications for faculty development.
        Advances in Simulation. 2018; 3 (Doi: https://doi.org/): 28https://doi.org/10.1186/s41077-018-0086-1
      3. David L. (2014). Social development theory (Vygotsky): Learning Theories. Retrieved from: https://www.learning-theories.com/vygotskys-social-learning-theory.html.

        • Doran G.
        There's a S.M.A.R.T. way to write management's goals and objectives.
        Management Review. 1981; 70: 35-36
        • Lawlor K.
        • Hornyak M.
        SMART goals: How the application of SMART goals can contribute to achievement of student learning outcomes.
        Developments in Business Simulation and Experiential Learning. 2012; 39: 259-267
        • Abuaiadah D.
        • Burrell C.
        • Bosu M.
        • et al.
        Assessing learning outcomes of course descriptors containing object-oriented programming concepts.
        New Zealand Journal of Educational Studies. 2019; 54: 345-356https://doi.org/10.1007/s40841-019-00139-y
      4. Center for Disease Control and Prevention (2009, January). Evaluation briefs: Writing SMART objectives. Retrieved from: http://www.cdc.gov/healthyyouth/evaluation/pdf/brief3b.pdf.

        • Carey J.M.
        • Rossler K.
        The How When Why of High-Fidelity Simulation.
        StatPearls Retrieved from. 2020;
        • Chiniara G.
        • Clark M.
        • Jaffrelot M.
        • Posner G.D.
        • Rivière É.
        Moving beyond fidelity.
        Clinical Simulation. Elsevier, 2019: 539-554
        • Hontvedt M.
        • Øvergård K.I.
        Simulations at work—A framework for configuring simulation fidelity with training objectives.
        Computer Supported Cooperative Work (CSCW). 2020; 29: 85-113
        • Engström H.
        • Hagiwara M.A.
        • Backlund P.
        • Lebram M.
        • Lundberg L.
        • Johannesson M.
        • Sterner A.
        • Söderholm H.M.
        The impact of contextualization on immersion in healthcare simulation.
        Advances in Simulation. 2016; 1: 1-11
        • INACSL Standards Committee
        INACSL standards of best practice: SimulationSM simulation design.
        Clinical Simulation in Nursing. 2016; 12 (http://dx.doi.org/): S5-S12https://doi.org/10.1016/j.ecns.2016.09.005