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Record W2986107739 · doi:10.1177/1049731519885015

Simulation in Social Work Education: A Scoping Review

2019· review· en· W2986107739 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

VenueResearch on Social Work Practice · 2019
Typereview
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChecklistBest practiceSocial workMedical educationMultimethodologyPsychologyQualitative researchManagement scienceMedicinePedagogySociologyEngineeringManagementSocial science

Abstract

fetched live from OpenAlex

Purpose: This article presents a scoping review that synthesized empirical studies on simulation in social work (SW) education. The review maps the research examining characteristics of simulation studies in SW education and emerging best practices. Method: Using Arksey and O’Malley’s scoping review framework to develop the methodology and following the PRISMA-ScR checklist, we selected 52 studies for this review. Results: Most studies were published in North America and included quantitative (37%), qualitative (31%), and mixed methods (33%). Simulation was used to teach generalist and specialized practice with interprofessional practice as the highest area of specialization. Simulation was also used for assessment purposes, and the Objective Structured Clinical Examination was a commonly reported method. We identified several facilitators and barriers to using simulation effectively for teaching and assessment. Conclusions: Our analysis permitted us to identify emerging best practices that can be used to guide teaching. Implications for SW research, teaching, and practice are discussed.

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)0.008
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.793
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.011
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0010.003

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 head0.611
GPT teacher head0.689
Teacher spread0.078 · 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