Partnering with a child welfare agency to design and deliver a BSW child welfare course: mixed results and lessons learned
Bibliographic record
Abstract
This paper describes a collaboration between a university and a local child welfare agency in the development and delivery of a Bachelor of Social Work (BSW) child welfare course featuring lived experience knowledge and learning situated in community-based child welfare sites. We present results of an exploratory mixed methods pre-post survey and focus group evaluation which examined changes in student general knowledge, perceptions of skills for decision- making and orienting beliefs about child welfare families. The results did not yield uniform significant results. The discussion delves into lessons learned about the importance of supporting students to develop skills for respectfully gathering and critically analyzing information and how to break through what we observed as student reluctance to learn about child welfare wrongdoings. We reflect on why, despite centering curriculum on lived experience knowledge, we were not successful in overturning students’ negative orienting beliefs about families and children receiving child welfare services. We conclude with considerations for new directions in BSW child welfare curriculum.
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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 | 0.044 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".