Center conducting review:
New South Wales Centre for Evidence Based Health Care
Name: Samuel Lapkin
Telephone: +61 243 551 569
Name: Dr Ritin Fernandez
Dr Tracy Levett-Jones, Senior Lecturer, Deputy Head of School (Teaching and Learning), The University of Newcastle, School of Nursing and Midwifery, Faculty of Health, Australia Dr Helen Bellchambers, Senior Lecturer, The University of Newcastle, School of Nursing and Midwifery, Faculty of Health, Australia
Support for this project has been provided by the Australian Learning and Teaching Council Ltd, an initiative of the Australian Government Department of Education, Employment and Workplace Relations. The views expressed in this paper do not necessarily reflect the views of the Australian Learning and Teaching Council.
The reviewers also wish to acknowledge the other members of the Australian Learning and Teaching Council project team: Dr Kerry Hoffman, Dr Sharon Bourgeois, Dr Sharyn Hunter, Dr Jennifer Dempsey, Dr Sarah Jeong, Noelene Hickey, Carol Norton, Raelene Kenny, and Karen Jeffrey.
Commencement date: February 2009
Expected Completion date: September 2009
In the nursing literature, terms such as clinical reasoning (CR), clinical judgement, problem solving, decision-making and critical thinking are frequently used interchangeably1,2. For the purpose of this review, the term CR will be defined as the process by which nurses collect cues; process the information; come to an understanding of a patient problem or situation; plan and implement interventions; evaluate outcomes and reflect on and learn from the process3-6. This definition indicates that CR is a process that involves both cognitive (or critical) thinking and metacognition (or reflective thinking). Development of CR skills enhances the nurse's ability to build on previously acquired knowledge or past experiences in order to address new or unfamiliar situations7.
Nurses with effective clinical reasoning skills have a positive impact on patient outcomes; conversely, those with poor clinical reasoning skills often fail to detect impending patient deterioration resulting in a “failure-to-rescue”8. However, providing the appropriate learning opportunities for nursing students to develop clinical reasoning skills is challenging. This is due, in part, to the increasingly complex and unpredictable nature of contemporary healthcare environments9,10. This challenge is intensified by factors such as competing demands for clinical placements11, clinical educators that too “often do not have time to think through clinical problems with students”12, and ethical constraints that require clinical skills to be developed without potential detriment to healthcare consumers physical condition. 13. Collectively, challenges such as these demand a re-examination of the teaching methodologies for maximising the development of CR by students in undergraduate nursing programs14.
One strategy that is increasingly being adopted to address the issues outlined above, is the use of simulation technologies15. Although there are numerous definitions of simulation, the one described by Gaba16 has been adopted for this review. Gaba, defines simulation as a technique used “to replace or amplify real experiences with guided experiences that evoke or replicate substantial aspects of the real world in a fully interactive manner”16. Simulation can range in complexity from simple case studies to fully computerised high-fidelity human patient simulation manikins (HPSMs)17.
Evidence indicates that the use of simulation achieves quality outcomes where the potential for error and large-scale disaster is high18. Well-known examples are flight simulations in aviation, training exercises in the military, and the development of nuclear power energy19,20. The first available documented evidence on the use of human patient simulation manikins in clinical education was in 1969 when Denson and Abrahamson used ‘Sim One'TM to supplement the training of anaesthetists21,22. Since then, various HPSMs have been developed and are broadly classified into three categories based on defined levels of fidelity; that is low, medium and high-fidelity23.
Fidelity in this context refers to the extent to which the simulation model resembles a live human. Low-fidelity HPSMs are static models or task trainers primarily comprised of rubber body parts which are used for the practice of clinical skills such as intravenous cannulation, urinary catheterisation and basic life support24,25. Medium fidelity HPSMs are full body manikins that have embedded software and can be controlled by an external, hand held device. They have more realism than the low-fidelity HPSMs. An example is Laerdal's Nursing AnneTM with VitalSim capability used in nursing education as a tool for introducing and developing more complex skills23 such as auscultation of normal and abnormal heart, breath and bowel sounds and identification of life-threatening cardiac dysrhythmias using electrocardiograph.
High-fidelity, HPSMs are life sized computerised manikins with realistic anatomical structures and high response fidelity11. They can mimic diverse parameters of human anatomical physiology, for example changes in cardiovascular, pulmonary, metabolic and neurological systems and have the ability to respond to nursing or pharmacological interventions in real time21,23,26,27. High-fidelity HPSMs are currently used in specialist medical fields such as anaesthetics and critical care and more recently in undergraduate nursing and allied health programs13,28. Examples of HPSMs include Laerdal SimManTM and METITM manikins.
Previous systematic reviews on the use of simulation in clinical education and training have not included CR as a clearly defined outcome measure9,28,29. While a review undertaken by Laschinger et al.,28 did include critical thinking as an outcome measure in clinical education, it did not focus on the effectiveness of using HPMSs in the teaching of CR skills to undergraduate nursing students. Similarly, an integrated literature review on the use of simulation in health education9 did not include studies that had undergraduate nursing students as participants. A systematic review by Issenberg et al.,29 considered the features of high-fidelity simulation that affected learning by pre and post-graduate medical students; besides not focusing on undergraduate nursing students the studies reviewed addressed knowledge acquisition rather than CR. Another systematic review being undertaken by Harper, Murrell and Elliott30 is considering qualified allied health staff and CR is not one of the outcome measures.
The dynamic nature of contemporary healthcare settings require nursing graduates to assume more complex roles which, in turn, necessitates the acquisition of more advanced CR abilities during their undergraduate education10. Alinier et al.,11 states that in the future, newly qualified nurses will be expected to be competent in handling emergencies and clinical events after having practised mainly with HPSMs. This situation is similar to the aviation industry where pilots are able to fly passenger planes and manage a variety of emergency events after having only practised on flight simulators27. It is essential therefore, that this systematic review fully explores the current state of knowledge regarding the effectiveness of HPSMs in the teaching of CR skills to undergraduate nursing students.
Aim of the review
The aim of this systematic review is to identify the best available evidence on the effectiveness of using human patient simulation manikins in the teaching of clinical reasoning skills to undergraduate nursing students.
Types of participants
The review will consider studies that include undergraduate nursing students. Studies that consider other allied health care professionals will be excluded unless data for undergraduate nursing students are included but analysed separately.
Types of intervention(s)/phenomena of interest
The intervention of interest is the use of HPSMs in undergraduate nursing education.
Types of outcome
The primary outcome measure is CR, as assessed by methods such as objective structured clinical examinations (OSCEs) and questionnaires. Other outcome measures related to education such as student satisfaction, knowledge acquisition, and psychomotor skills competence will be included to provide a broader perspective on the use of HPSMs in undergraduate nursing education.
Type of studies
The systematic review will primarily consider randomised controlled trials (RCTs), however in the absence of RCTs, other research designs, such as non-randomised controlled trials and before and after studies, will be considered. Studies using these methodologies will be included in a narrative summary to enable the identification of current best evidence regarding the effectiveness of HPSMs in undergraduate nursing education.
The search strategy aims to find both published and unpublished studies, limited to the English language and restricted to the last ten years. A three-step search strategy will be utilised in each component of this review. Initially a limited search of MEDLINE and Proquest will be undertaken followed by an analysis of the text words contained in the title and abstract, and of the index terms used to describe the article.
Initial keywords to be used:
Human patient simulator,
Teaching, and Training
The second step will involve searching electronic databases using several combinations and permutation of key words and index terms identified by the initial literature scoping. Where appropriate, key words will be exploded and truncated. Using a defined search and retrieval method, the databases to be searched are:
Dissertation & Theses
Proquest Nursing Journals
The third stage involves the hand searching of the following multiple sources to find any additional articles:
- Reference lists in those publications identified in the first two stages. This process will also identify the most recent publications not yet cited by other publications or electronic databases and any further articles missed in database searches.
- The Journal of the Society for Simulation in Healthcare is a multidisciplinary publication encompassing all areas of healthcare simulation technology.
- The Simulation Innovation Resource Center, a bibliography facility that offers annotations of publications related to simulation topics.
- Mednar.com, Conference Proceedings, Dissertation Abstract, Reports and discussion papers for other ‘grey literature'.
The bibliographical software package EndnoteTM will be utilised to manage all references as it facilitates the importation of references from electronic databases as well as the linkage of references into the Joanna Briggs Institute (JBI) Comprehensive Review Management System (CReMSTM) for assessment of methodological quality.
Methods of the review
Assessment of methodological quality
Papers selected for retrieval will be assessed for methodological validity by two independent reviewers prior to inclusion in the review. For this process, the reviewers will use the critical appraisal instrument from JBI known as the Meta Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI) (Appendix I). Where agreement is not reached between the reviewers, a third reviewer will be consulted.
Data will be extracted from the papers included in the review using the standardised data extraction tool from JBI-MAStARI (Appendix I). The extracted data will include specific details about the interventions, populations, study methods and outcomes of significance to the aim of the review.
Quantitative papers will, wherever possible, be pooled in statistical meta-analysis using the JBI-MAStARI instrument. All results will be subject to double data entry to minimise errors. Odds ratio (for categorical data) and weighted mean differences (for continuous data) and their 95% confidence intervals will be calculated for analysis. Heterogeneity will be assessed using the standard Chi-square. Where statistical pooling is not possible the findings will be presented in narrative summary form.
Conflicts of interest
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JBI Critical Appraisal Checklist for Experimental Studies
Critical Appraisal instruments for Comparable / Case Control Studies
Critical Appraisal Instruments for Descriptive / Case Series Studies
MAStARI data extraction instruments
Data extraction instrument for Experimental Studies
Data extraction instrument for Comparable Cohort/ Case Control Studies
Data extraction instrument for Descriptive / Case Series Studies