'It's more than a sport's facility, it's a safety net for youth': Exploring the Utility of Trauma-and Violence-Informed Sport for Development (TVISFD) Programs with Maple Leaf Sport and Entertainment's LaunchPad
Bibliographic record
Abstract
Gender-based violence (GBV) has disproportionately impacted the lives of women, girls, and gender diverse people in Canada. In a partial response to escalating GBV rates, a growing movement towards trauma- and violence-informed (TVI) healthcare has emerged in Canada. TVI physical activity has been proposed as an effective approach to support individuals with trauma as an adjunctive treatment to usual care. The focus of this thesis was to explore how use of a TVI approach to Sport For Development programs at Maple Leaf Sports and Entertainments LaunchPad may support vulnerable youth. Semi-structured interviews and demographic surveys were conducted with fifteen (n=15) full-time staff, hourly youth workers, and program participants. Findings suggested that community member representation amongst staff enhanced participants and youth workers perceptions of safety. Program features that aligned with TVI physical activity included: 1) creating emotionally and physically safe environments; and 2) providing a capacity-building and strengths-based approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".