Canadian Social Work Field Education During a Global Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has significantly affected all aspects of social work education, including field education. The Transforming the Field Education Landscape (TFEL) partnership conducted two national online surveys to determine the impacts of the pandemic on social work field education. The first survey explored the perspectives of students and received responses from 367 Bachelor of Social Work (BSW) and Master of Social Work (MSW) students. The second survey was designed to gather the perspectives of field instructors, with 73 field instructors completing the survey. The article examines the impacts of the pandemic on social work practice, field supervision, practicum flexibility and accommodations in placement, the shift to remote learning, perceptions of future career prospects and the effects on wellness and mental health. This article contributes to an increased understanding of the strengths and challenges facing social work field education, and informs field planning and responses in a pandemic. The findings will be of interest to social work field education programmes, field education coordinators and directors, field instructors, field agencies and undergraduate and graduate social work students. Recommendations for social work field education are offered.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".