BEYOND THE CHALLENGES: NEW INSIGHTS AND INNOVATIONS IN FIELD EDUCATION
Bibliographic record
Abstract
The International Conference on Social Work and Fieldwork Education in Hong Kong was organized to discuss the need for change and innovation in social work education with a particular focus on field education. There is a need for social work field educators to identify innovative, promising, and wise practices in field education. In many contexts, field education is challenged to procure sufficient placements each year. This growing demand for placements has created numerous challenges in field education programs. In response to the challenges facing social work field education, and the need to develop sustainable models of field education, the Transforming the Field Education Landscape (TFEL) project was formed. The TFEL project is a partnership designed to integrate research and practice through the development of partnered research training initiatives aimed at enhancing student research practice knowledge and applied skill development. In Canada, many field education challenges were amplified due to the COVID-19 pandemic, which required social work programs to adapt in order to navigate unprecedented circumstances. This article discusses the challenges facing field education programs and provides an overview of the TFEL project, with a focus on how the partnership is addressing these concerns. It defines what is meant by innovative, promising, and wise practices in field education, and how these innovations can assist in preparing the next generation of social workers to become highly qualified personnel.
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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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.020 | 0.027 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".