Peeling back the layers: examining the trauma-informed programming experiences of staff in a sport for development and peace organization
Bibliographic record
Abstract
An increasing number of Sport for Development and Peace (SDP) organizations are aiming to offer trauma-informed programming (TIP). However, there is limited understanding of the specific efforts made by SDP staff to put TIP into practice. This paper explores the implementation of TIP by staff at Free Play (FP), a non-profit SDP organization based in Edmonton, Alberta. Drawing from a two-year, Community-Based Research (CBR) project, we critically analyze the experiences of FP staff as they navigate the entanglements of the ethical imperative of delivering TIP, and a broad array of social services to youth and their families, alongside the organization’s growth agenda within the competitive non-profit industrial complex shaped by the market-driven logic of neoliberalism. These contradictions set decisive limits and pressures on staff who recognize the value and importance of TIP, but who struggle with the heavy emotional labour of implementing it, especially without ongoing training and support, and while working under precarious conditions. Our analysis, in turn, provides recommendations for managers and staff of SDP organizations on how to better support TIP implementation.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.025 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".