Integrating Practice Research into Social Work Field Education in Canada
Bibliographic record
Abstract
Given the increasing value placed on research among social work practitioners, and that field education is primarily responsible for the integration of social work values, knowledge, and practice, it is essential that research skills be incorporated into BSW and MSW field practica. In 2020, a team of faculty co-investigators and students explored the integration of research activities into Canadian BSW and MSW field practica through the review of online field education materials of all accredited programs. We make the argument that not only is it essential for professional social workers to receive training in practice research, but also that there is much room for us to integrate such research into field education. We begin with a review of the literature regarding social work student attitudes toward research, widely known to be hesitant and even hostile, before discussing the limited international literature (English and French) on the experience of integrating research into field education. After describing our methods, we then present our findings in terms of BSW and MSW programs at Canada’s anglophone and francophone universities. We conclude with a discussion of the implications in terms of ways to increase the role of research in field practica.
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 | 0.051 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.030 | 0.017 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".