Supporting child survivors of trauma at school: depathologizing behaviour and educating teachers
Bibliographic record
Abstract
Childhood trauma is a substantial concern in our education system in Ontario, as it has \nbeen noted that approximately 32% (Afifi et al., 2014) to 36% (Findlay & Sutherland, 2014) of \nCanadian adults report that they were exposed to abuse as children. Trauma can have significant \nimpact on a child’s learning (Vasilevski & Tucker, 2016), behaviour (Greeson et al., 2014), and \nwellness (Roberts, Ferguson, & Crusto, 2013), and puts them at an increased risk of being \nretraumatized or further punished in schools due to the Western education system relying on the \nbehavioural model (Costa, 2017). A 450-hour social work practicum was completed with the \nMental Health Team at the Sudbury Catholic District School Board (SCDSB) as a partial \nrequirement of the Laurentian University MSW program. This practicum project report employs \nstructural and anti-oppressive social work perspectives and a trauma theory lens to undergo an \nexploration into: (a) what trauma-informed practices (TIPs) and primary models are used by the \nSCDSB to inform their practice in supporting students who have been exposed to trauma, (b) to \nwhat extent school-based social work in this setting reflects certain models that function to \nfurther harm child survivors of trauma, such as the behavioural model, and its relationship to \nunderstanding student experiences through the lens of trauma, and (c) how trauma theory can be \nused to establish alternatives to pathologization in regards to children within schools who have \nexperienced trauma. Trauma-informed professional development lunch-and-learns were \npresented to teaching staff in four schools as the intervention provided during this practicum
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".