Transforming the Field Education Landscape: national survey on the state of field education in Canada
Bibliographic record
Abstract
The Transforming the Field Education Landscape (TFEL) project conducted a survey to gather information from field education coordinators and directors (FECDs) about their field education programs, staffing models, resources, and activities, and to invite their perspectives on field education in Canada. The purpose of this mixed methods study was to better understand the state of social work field education in Canada, from the perspectives of FECDs in accredited Canadian social work education programs. The study used an adapted version of a survey instrument developed by the Council of Social Work Education (CSWE) in the United States in 2015. Field education staff at 39 of the 43 accredited programs in Canada completed the survey. Results revealed differences in staffing and program administration models based on program size, and highlighted the workloads and challenges experienced by FECDs in facilitating quality practicum opportunities. The results show that FECDs are engaged in unique activities and responsibilities within social work education programs. The impacts of funding cutbacks, student readiness for placement, and resource shortages, including staff, time, institutional support, and placement disruptions, are among the challenges discussed in this article. The findings establish a baseline on the state of social work field education in Canada.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".