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Record W4391019467 · doi:10.1016/j.ecns.2023.101504

Location! Location! Location! Comparing Simulation Debriefing Spaces

2024· article· en· W4391019467 on OpenAlexaff
Sufia Turner, Rasheda Rabbani, Nicole Harder

Bibliographic record

VenueClinical Simulation in Nursing · 2024
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsGeorge & Fay Yee Centre for Healthcare InnovationUniversity of Manitoba
Fundersnot available
KeywordsDebriefingEconomic shortagePsychologyNurse educationNursingMedical educationMedicineSocial psychology

Abstract

fetched live from OpenAlex

Background Space is a commodity that is in high demand for both theoretical education and performance-based education. With the global nursing shortage, nursing educators are seeing an increase in their enrollment numbers making space more difficult to attain. We know that an integral component of nursing simulation is the use of deliberate reflection in the post-simulation debriefing. However, there have been very few comparisons and no studies were found on how the location of the debriefing effects the student experiences. Methods Using the Debriefing Experience Scale, a quasi-experimental design with a convenience sample of 4th-year undergraduate nursing students compared the experience of debriefing in one room versus using multiple rooms. Results Two areas were identified as statistically significant; "the importance of the debriefing to help make connections between theory and real life" and "the debriefing environment was physically comfortable." Conclusion Debriefing experiences were largely the same. The two items that were identified as different need to further be explored to determine how this might affect participants' simulation debriefing experience.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.140
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.140
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0140.001

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 head0.141
GPT teacher head0.522
Teacher spread0.380 · 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 source (direct Gemma or distilled Codex), 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".

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Citations1
Published2024
Admission routes1
Has abstractyes

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