Social Work Curriculum Review Case Study: Service Users Tell Us What Makes Effective Social Workers
Bibliographic record
Abstract
This paper presents the findings from community focus groups, comprised of social service users, and explores the characteristics of effective social workers. Focus groups were conducted as part of a case study to inform a Master of Social Work (MSW) curriculum review at Wilfrid Laurier University’s Faculty of Social Work. Wilfrid Laurier University has two MSW programs—the MSW Aboriginal Field of Study (AFS) and a non-Aboriginal program. The case for this study was the non-Aboriginal MSW program. Ongoing program evaluation that includes feedback from service users honours the knowledge of marginalized communities, and is an accreditation requirement of the Canadian Association for Social Work Education (CASWE). Four focus groups were conducted with a total of 24 individuals who access programs from human service organizations that provide supportive housing, immigrant, or refugee services in the Kitchener-Waterloo area. Service users identified numerous characteristics of effective social workers, including kindness, cultural awareness, and strong communication skills, as well as the need to articulate and address issues of professional suitability. We conclude by querying whether the typical assessment of MSW students’ suitability for the profession is adequate, and provide the AFS wholistic and comprehensive evaluation as an example of an alternative approach to MSW student assessment.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".