Impact of equity-centered training: supporting racialized communities with enhanced education for social workers
Bibliographic record
Abstract
Social workers benefit from increasing their skills and knowledge working with a diverse range of communities and populations, particularly among racialized and marginalized communities. This study evaluates an Ontario-based project on equity-centered training to social workers with a focus on the experiences of adult racialized learners. Data was collected through a convenience sample of participants completing post-training evaluation surveys which measured learner’s satisfaction, learning, and confidence. We delivered 53 trainings to over 3,000 learners and a total of 670 surveys were analyzed. Respondents were approximately 41% racialized, 40% aged 45 and under, 84% female, and 39% with over 15 years of experience. Compared to non-racialized learners, racialized learners reported higher satisfaction with an increased willingness to apply knowledge to practice. In addition, racialized learners reported a higher increase in skills developed and confidence, including interest in specific areas like intergenerational trauma. All learners shared the importance of critical self-reflection and awareness, appreciation for practical strategies, and openness to ongoing learning. It is important to offer equity-centered training to social workers that increase skills and knowledge in developing inclusive mental health services. Professional development can complement formal education in meeting the needs of increasingly diverse communities.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".