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Record W4405019508 · doi:10.1016/j.ecns.2024.101666

Developing virtual gaming simulations for complex clients with substance use through international collaborations

2024· article· en· W4405019508 on OpenAlexaffabout
Kateryna Metersky, Michelle Hughes, Caitlin Cosgrove, Esther Bodach, S. Ferguson, Susana Neves-Silva, Sherry Espin, Alison Smart, Outi Lastumäki, Sanna Sandström, Essi Varkki

Bibliographic record

VenueClinical Simulation in Nursing · 2024
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsCentennial CollegeUniversity of Toronto
Fundersnot available
KeywordsSubstance useInternet privacyPsychologyHuman–computer interactionBusinessComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

Background The aim of this paper was to describe how six nurse educators and three nursing students from Canada, Northern Ireland and Finland developed a virtual gaming simulation (VGS) on a client with a complex medication profile to fill a gap in an undergraduate nursing curriculum. The VGS navigates learners to engage in a scenario with a client admitted for an acute exacerbation of chronic obstructive pulmonary disease. Method The international collaboration occurred through continuous dialogue and reflective practice to ensure the inclusion of country-specific practices and laws. Lessons Learned The international collaboration allowed educators and students to take a unified approach to address country specific best practices, such as medication administration and the intricacies of cannabis legality. A theoretical lens enhanced the development and structure of the VGS. The student voice provided a holistic perspective. Conclusion International collaborations with nurse educators and students can enhance the VGS design process by facilitating diverse perspectives. This VGS invited learners to engage in a clinical scenario to learn about the importance of providing person-centered care to a client with a complex profile.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.647
Threshold uncertainty score0.677

Codex and Gemma teacher scores by category

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

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 head0.325
GPT teacher head0.569
Teacher spread0.244 · 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 designSimulation or modeling
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 routes2
Has abstractyes

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