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Record W2306704421 · doi:10.5116/ijme.56ed.1060

Identification tags and learners’ situational awareness during high-fidelity simulation

2016· article· en· W2306704421 on OpenAlexaffabout
Issam Tanoubi, Marie‐Ève Bélanger, Leonardo Georgescu, Roger Perron, Jean‐François Germain, Arnaud Robitaille, Pierre Drolet

Bibliographic record

VenueInternational Journal of Medical Education · 2016
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversité du Québec à MontréalUniversité de MontréalMontreal Clinical Research Institute
Fundersnot available
KeywordsSituation awarenessIdentification (biology)Computer scienceFidelityHigh fidelityPsychologySituational ethicsData scienceSocial psychologyEngineeringBiology

Abstract

fetched live from OpenAlex

Simulation-based medical education is an integral part of the curriculum of many specialties. Simulation allows participants to develop and practice technical skills useful to the management of rare and urgent clinical situations in a safe and supportive learning environment.1 Several points are deemed essential to the promotion of optimal learning during simulation. One, which was emphasized by Issenberg and associates, stated that acquiring experience in medicine was governed by the learners’ commitment to a simulation’s realistic environment.2 Obtaining and maintaining the commitment of participants in a high-fidelity simulation scenario is then essential for a better learning experience. One way to foster a learner’s commitment toward a simulated environment is to help the learner gain a greater awareness of the environment’s characteristics. With better situational awareness, participants can more easily understand the various elements and the complexity of the environment, and then anticipate the human and material resources that might be needed. This could allow students to engage in better clinical decision making. Situational awareness has been measured by quantitative scales in studies conducted in the aviation and maritime fields, but additional adjustment and validation of these scales are still required.3,4 Moreover, specific factors affecting the situational awareness remain unstudied. Here, we undertook this research to assess whether wearing badges or name tags stating each participant’s function and posting a sign to clearly inform students of the scenario’s location during the high-fidelity simulation could lead to greater awareness of each learner’s role and contribute to their commitment. We hypothesized that using badges and formally identifying the location of the scenario would enable participants to feel more committed and to better identify everyone’s role and where the action was taking place. Evaluating the importance of the name tags and place identification We used a pretest-posttest design to achieve our study goals. Our study took place within a planned simulation training course for 25 anesthesiology residents on the management of critical anesthesia situations from April 30, 2014, to June 11, 2014. Study coordinators assigned 4 to 5 participants to each of the 6 overall simulation sessions scheduled. Each session was 4 hours long and included an introduction, followed by two high-fidelity simulated scenarios with debriefing. The high-fidelity simulation was operationalized through high equipment fidelity (functionality and responsiveness of patients, manikins, and medical instruments), high environment fidelity (real world overload demands), and high psychologic fidelity (maintaining the natural ‘‘flow’’ of a clinical scenario and participant’s immersion within the scenario).5 One scenario was about a patient who had a massive amniotic fluid embolism that occurred after a complicated delivery and leading to a maternal cardiac arrest, and the alternative scenario was of a patient with postoperative malignant hyperthermia. The order of the two scenarios was randomized for each session. During the first scenario of the session, participants did not wear badges, and no sign that formally identified the mock location was allowed. In the second scenario, name badges and identifying signs were used systematically. At the end of each scenario, all residents completed, using an audience response system (Turning Point, Ontario, Canada, Turning Technologies), a 7-question survey  (7-point Likert scale) to evaluate their situational awareness and their level of commitment. An evaluator, blinded to the randomization sequence, listened to the soundtrack only of the audio-video recording of the simulated scenarios in search of indicators suggesting a lack of situational awareness. We noted no differences between the groups regarding their consciousness of the location, or of the various roles played by the participants. We found that the resident’s engagement was also similar in both groups. The number of indicators of poor situational awareness, noted by the blinded evaluator during audio review of the scenario’s video recording was not significantly different between groups.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.424
Teacher spread0.395 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2016
Admission routes2
Has abstractyes

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