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Record W4361007859 · doi:10.1080/02615479.2023.2194318

Simulation in social work education: a qualitative study of standardized clients’ experiences

2023· article· en· W4361007859 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

VenueSocial Work Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsCarleton University
Fundersnot available
KeywordsEnthusiasmSocial workCompetence (human resources)Qualitative researchPsychologyPedagogyMedical educationWork (physics)SociologyEngineering ethicsSocial psychologyMedicineEngineeringSocial sciencePolitical science

Abstract

fetched live from OpenAlex

This explorative study provides insight into the experiences of standardized clients (SC) involved in simulation for social work education and research. The purpose was to contribute to scholarly conversations regarding SCs and to provide an empirical foundation to inform our simulation program by attending to the perspectives of SC participants who are key to simulation-based learning (SBL). A qualitative, cross-sectional survey was employed to understand these experiences. In doing so, this study attempts to ensure SBL design and implementation is more attentive to SCs who increasingly represent marginalized communities. SCs (n = 14) reported an enthusiasm for engaging with social work simulations as they find the work both interesting and meaningful. The findings demonstrate how incorporating SCs into teaching teams fosters a community of practice that can function to incorporate members of marginalized communities into professional education, and reshape pedagogical strategies for supporting the development of holistic competence.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.013
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.088
GPT teacher head0.500
Teacher spread0.412 · 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