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Record W4391644097 · doi:10.7202/1108988ar

TRANSFORMING FIELD EDUCATION: VOICES OF FIELD EDUCATORS IN CANADA

2024· article· en· W4391644097 on OpenAlexvenueaboutno aff
Julie Drolet, Liz Tettman, Hanna Hameline, Vibha Kaushik, Kamal Khatiwada, Shannon Klassen, Emmanuel Chilanga, Sheri M. McConnell, Eileen McKee, David Nicholas, Christine A. Walsh

Bibliographic record

VenueCanadian social work review · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsnot available
Fundersnot available
KeywordsField (mathematics)Agency (philosophy)Public relationsSocial workGovernment (linguistics)RestructuringSociologyTransformative learningWork (physics)Thematic analysisPolitical scienceProfessional developmentPedagogyQualitative researchSocial scienceEngineering

Abstract

fetched live from OpenAlex

Social work field educators are facing new challenges and opportunities that require innovative approaches to transform social work field education. Field education is critical to student learning, and in turn, social work practice. In Canada, field education is in crisis, due in part to growing social work student enrolments, government cutbacks to post-secondary education, limited resources, and organizational restructuring, all of which contribute to a reduced number of field placements in agency settings. The objective of this study is to respond to this situation by engaging field educators to better understand what is needed to transform field education in Canada. Researchers asked three to five questions in 31 focus groups discussion sessions with field educators nationally. The responses were coded using thematic analysis. This article presents three themes that are critical to the transformation of field education: innovative practices for field education, impacts of COVID-19, and decolonization of field education. The implications and recommendations call on the collaboration of field education stakeholders across Canada to expand understanding about the critical role of field education in organizations and the profession, and in turn, nurture new field learning opportunities.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.953
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.353
Teacher spread0.330 · 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.

Study designNot applicable
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

Citations1
Published2024
Admission routes2
Has abstractyes

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