TRANSFORMING FIELD EDUCATION: VOICES OF FIELD EDUCATORS IN CANADA
Bibliographic record
Abstract
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 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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".