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Record W4389891661 · doi:10.3928/00220124-20231211-03

GridlockED as an Intervention for Nurses (GAAIN) Study

2023· article· en· W4389891661 on OpenAlexaff
Teresa M. Chan, Nicole Bodnariuc, Nandini Nandeesha, Jennifer Kodis, C. M. O’Connor, Shawn Mondoux, Alim Pardhan, Ruth Chen

Bibliographic record

VenueThe Journal of Continuing Education in Nursing · 2023
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPsychologyIntervention (counseling)NursingMedical educationMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: GridlockED (The Game Crafter, LLC) is a serious game that was developed to teach challenges that face nursing and medical professionals in the emergency department (ED). However, few studies have explored nurses' perceptions of the utility, fidelity, acceptability, and applicability of the serious game modality. This study examined how ED nurses view GridlockED as a continuing education platform. METHOD: This single-center observational study explored how nurses engage with and respond to Grid-lockED. The convenience sample included participants recruited from a local continuing nursing education day. Participants completed a presurvey, engaged in a full game play session with the GridlockED game for approximately 45 minutes, and immediately completed a post-game play survey. RESULTS: Of the 48 participants (11 male, 37 female; 44 of 48 were RNs), most (91%) agreed that the workflow reflected in the game was equivalent to the flow in a typical ED. Almost all (96%) found the cases in the game reflective of real ED patients, and most (92%) found the game a useful educational tool to prepare new nurses to transition into the ED environment. CONCLUSION: .

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.025
GPT teacher head0.441
Teacher spread0.416 · 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 designOther design
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

Citations4
Published2023
Admission routes1
Has abstractyes

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