Learning a Trauma-Sensitive Sport Model: Programme Implementation Experiences
Bibliographic record
Abstract
This study investigates the learning experiences of youth sport leaders as they implemented a trauma-sensitive sport model in a Canadian youth-serving organisation. Using Wenger-Trayner and Wenger-Trayner’s value-creation cycle, the study assesses the value generated through leaders’ participation in ongoing social learning opportunities and programme implementation. Two cohorts of leaders participated in this study, and data were collected through interviews, knowledge surveys, and communications on an online workspace. The qualitative thematic analysis offered insights of the diverse learning interactions and value generated in these interactions, and these findings were further complemented by the quantitative findings. The results indicated that leaders encountered various learning interactions (e.g., training workshops, applied practice, and peer discussions) and generated in immediate (e.g., enjoyment and peer relationships), applied (e.g., programme facilitation skills, supporting youth’s learning, and youths’ receptivity), realised (e.g., leaders’ knowledge and youths’ behaviour change), and transformative values (e.g., transfer of leaders’ skills and influence on club culture and practices). The study contributes valuable insights into applying trauma-sensitive models in youth sports, emphasising the importance of varied learning interactions and outcomes.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".