Integrating a Sport-Based Trauma-Sensitive Program in a National Youth-Serving Organization
Bibliographic record
Abstract
There is a pressing need to equip youth-serving community organizations to respond to the unique needs of trauma-exposed children. Early prevention measures can be an effective means of redirecting children to self-regulatory healing, while facilitating their transition toward strength-based thriving. Sport can offer a powerful opportunity to reach these children; however there remains little information on how to effectively develop, deliver, evaluate, and sustain trauma-sensitive sport programs in a community context. The purpose of this paper is to outline a case study of integrating sport-based trauma-sensitive practices with BGC Canada's national Bounce Back League program. An interdisciplinary partnership of academic, community, and practice experts used a community-based participatory action research approach, paired with a knowledge translational approach, to guide the process of program development. Mixed methods (e.g., surveys, logbooks, interviews, focus groups, online communications) were used to generate ongoing insights of staff's training experiences, successes and challenges of program implementation, and potential impact of program on club members. Several stages of program development are described, including: (a) collaboratively planning the program; (b) piloting the program to three clubs; (c) adapting the program using pilot insights; (d) expanding the adapted program to ten clubs; and (e) creating opportunities to maintain, sustain, and scale-out practices throughout grant duration and beyond. Lessons learned regarding the leadership team's experiences in terms of developing, adapting, and integrating trauma-sensitive practices in this community context are shared. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10560-021-00776-7.
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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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".