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Record W2887732298 · doi:10.24908/pceea.v0i0.9696

Using Role-Playing Simulations to Teach Quality Control in the Design of Medical Devices

2018· article· en· W2887732298 on OpenAlexafffundvenue
J. Christopher Bouwmeester, Vicki Komisar, Arushri Swarup

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Marketing Education
Canadian institutionsHospital for Sick ChildrenUniversity Health NetworkToronto Rehabilitation InstituteUniversity of Toronto
FundersUniversity of TorontoToronto Rehabilitation InstituteCanadian Institutes of Health ResearchAGE-WELL
KeywordsAgency (philosophy)Control (management)Quality (philosophy)Production (economics)BusinessMarketingUnit (ring theory)Knowledge managementOperations managementComputer scienceEngineeringEconomicsPsychologyMicroeconomics

Abstract

fetched live from OpenAlex

Abstract – A simulation is used to facilitate cooperative and team-based learning to introduce concepts of human factors, risk analysis, and quality control applied to the design of medical devices. We further use a friendly game-based approach to simulate the dynamics between a customer, a regulatory agency, and competitive manufacturers. Students are divided into manufacturing teams/companies and teaching assistants act as the customer and regulator. To promote positive interdependence and individual accountability, each student within a company is assigned roles of CEO, inspector, marketer, and designer. The goal for each company is to design and produce as many eye patch medical devices as possible, which must be approved by the regulator, within a tight deadline. Products are evaluated by the customer, who decides what price to pay for each unit, at the end of production. The most successful company is determined by the greatest amount of money earned after two rounds of production and sales.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.277
Teacher spread0.253 · 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

Citations2
Published2018
Admission routes3
Has abstractyes

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