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Record W3045275270 · doi:10.1002/aet2.10510

Experiences of Participants During In Situ Simulation With a Learner Present

2020· article· en· W3045275270 on OpenAlexaff
Janatani Balakumaran, Ben Forestell, Krista Dowhos, Alim Nagji

Bibliographic record

VenueAEM Education and Training · 2020
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDebriefingAffect (linguistics)Medical educationFocus groupPsychologyHealth careQualitative researchMedicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: While in situ simulation (ISS) provides robust value for health care teams, it is less clear how medical learners affect the experiences of other participants. METHODS: This was a single-center qualitative analysis of a community hospital's emergency department ISS program that included medical learners (medical students, family and emergency medicine residents). Focus groups were conducted before and after with nurses, staff physicians, and resident physicians. Phenomenologic analysis using a constructivist framework was used to examine themes. RESULTS: Fifty-two ISSs were held from February 2019 to March 2020. Of those simulations, 36 had learners present. Positive effects included creating an open learning environment and offering staff physicians a safe teaching space. Negative effects arose when objectives of ISS were highly team based or latent safety threat (LST) focused. All groups thought learners added value to ISS. CONCLUSION: Thought should be given to ISS objectives when considering how learners affect other participants. When including learners, review objectives, clarify expectations in prebriefing, and ensure that debriefing begins with LSTs and process objectives before diving into medical objectives. This study provides insight into the effect of medical learners on ISS.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

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

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 head0.136
GPT teacher head0.408
Teacher spread0.272 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2020
Admission routes1
Has abstractyes

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