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Simulation Innovation in Cyberspace: A Collaborative Approach to Teaching and Learning in Child and Youth Care Education

2019· article· en· W4400898310 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePapers on postsecondary learning and teaching. · 2019
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCyberspaceChild carePsychologyMathematics educationPedagogyKnowledge managementMedical educationComputer scienceThe InternetMedicineWorld Wide WebNursing

Abstract

fetched live from OpenAlex

Leveraging digital technology for practice innovation is a compelling challenge. Limited education and training prevent human service practitioners from incorporating technology into practice. Progress in this area will be achieved when significant changes to pedagogy support technology integration with teaching/learning partnerships in higher education. With the recent attention to relational Child and Youth Care (CYC) practice in cyberspace (Martin & Stuart, 2011), this paper aims to highlight student/teacher explorations in this emerging area of clinical practice using student-driven simulated online counselling sessions supervised by the course instructor. Beyond critical learning within the roleplay activities, students engaged in solving disruptions to simulations, which can enhance their future agility in real practice situations (Rooney, Hopwood, Boud, & Kelly, 2015). Foundations in the Scholarship of Teaching and Learning (SoTL), experiential learning theory (ELT), and learner-led (LED) approaches guided student engagement with technology and reflexive practice in this graduate level classroom.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
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 head0.009
GPT teacher head0.353
Teacher spread0.344 · 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