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Record W4402527987 · doi:10.1002/aet2.11023

Evaluating <scp>ExpandED</scp> : Evaluating the effectiveness of a serious game expansion pack in teaching health professional students about interprofessional care

2024· article· en· W4402527987 on OpenAlexaff
Clare Fiala, Sowmithree Ragothaman, Gursukhmani Johl, Monica Sabbineni, Sarah Wojkowski, Teresa M. Chan

Bibliographic record

VenueAEM Education and Training · 2024
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsToronto Metropolitan UniversityPublic Health OntarioUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMedical educationHealth carePsychologyBusinessNursingMarketingMedicinePolitical science

Abstract

fetched live from OpenAlex

Abstract Background The emergency department (ED) is a challenging fast‐paced environment with high‐acuity, undifferentiated patients who often require extensive interdisciplinary care. This paper introduces ExpandED, an expansion pack to the serious board game GridlockED, designed to enhance players’ understanding of interprofessional collaboration in the ED and the diverse scope of practice of different ED professionals including physicians, residents, registered nurses, registered practical nurses, social workers, occupational therapists, and physiotherapists. This investigation evaluates the effectiveness of ExpandED as a teaching tool for medical and allied health professions students about interprofessional collaboration in the ED. Methods A program evaluation harnessing a playtest framework was employed. Participants completed pre‐ and postgame surveys including quantitative measures (e.g., Likert scales) and qualitative free‐text feedback that focused on participant familiarity with ED functioning, valuation of interprofessional collaboration before and after playing, and feedback on game usability and effectiveness. Results Recruitment was open to students in all health care and allied health professional programs at the institution. Forty‐five participants were recruited from medical doctor, nursing, physiotherapy, and speech language pathology programs. ExpandED enhances participants' understanding of ED workflow ( p &lt; 0.001) and provides an enjoyable playing experience. However, participants’ valuation of interdisciplinary teamwork did not change significantly before and after game play ( p = 0.17). Participants expressed satisfaction with the game's accuracy in simulating the ED environment and appreciated the opportunity to collaborate with peers from different disciplines. Challenges reported included some tension among players, potential biases, and limitations of fidelity to a real‐life ED. Conclusions While this study has limitations regarding participant sampling and duration of gameplay sessions, it highlights the potential of ExpandED for teaching interprofessional collaboration in the ED. These findings will guide further development to optimize the expansion pack's effectiveness and its implementation into health care curricula.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.009
metaresearch head score (Gemma)0.002
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 score0.694
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.071
GPT teacher head0.555
Teacher spread0.484 · 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

Labeled directly by 2 models reading the full record.

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

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other 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

Citations0
Published2024
Admission routes1
Has abstractyes

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