Impact of equity-centered training: supporting racialized communities with enhanced education for social workers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it