Partners in Education: Leveraging School Social Workers to Support Transformative Equity and Well-Being Work in an Ontario K-12 Public School Board
Bibliographic record
Abstract
School social workers (SSWs) have been an integral part of Ontario’s K-12 public education system for over one hundred years. Their unique training, skill set, and practice perspectives enables provision of comprehensive services benefitting the whole school community. The social work profession is grounded in a code of ethics committed to the advancement of social justice for all and draws from a rich history of critical theorizing and evidence-based practice to further this goal. These features of the profession ideally position SSWs to serve as leaders and partners in the school mission, particularly at a time when the need for transformative change to support all students is recognized. Yet SSWs frequently remain underutilized resources in schools. This Organizational Improvement Plan (OIP) addresses the problem of practice (PoP): the underutilization of SSWs as leaders and change partners at Leaders in Learning District School Board (LLDSB). As a former Mental Health Lead and current frontline social worker, union leader, professional practice facilitator, and member of key provincial advisory groups, I explore the organizational context at LLDSB and propose a solution to the PoP; a pilot project to promote role integration of SSWs using implementation science. Transformational and critical leadership perspectives underpin the approach to change. I develop a complementary framework for leading the change; a detailed implementation, monitoring, and evaluation plan supported by Plan-Do-Study-Act cycles; and a plan to communicate the need for change. I conclude by discussing how the change can be institutionalized and sustained beyond the pilot project.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".