Evaluation of Online Trauma- and Violence-Informed Physical Activity Training Modules
Bibliographic record
Abstract
BACKGROUND: Exposure to traumatic events can lead to adverse health outcomes, including depression, anxiety, post-traumatic stress disorder, and chronic pain. Physical activity can help alleviate many negative health concerns associated with trauma; however, exercise instructors and social service providers often lack the necessary training to support and improve access to physical activity for individuals who have experienced trauma. This study aimed to test the feasibility, acceptability, and potential effectiveness of newly developed training modules, designed to improve knowledge and confidence to apply Trauma and Violence-Informed Care within physical activity settings. METHODS: Participants were invited to take part in 6 co-designed online trauma- and violence- informed physical activity (TVIPA) training modules. Baseline and postintervention data were analyzed using a chi-square test of independence, assessing changes in participants' confidence of TVIPA knowledge and application. Participants were also invited to take part in a qualitative interview, 6 months post completing the training to examine how they applied the training. RESULTS: In total, 205 participants completed a baseline survey and 155 completed the postintervention assessment. A significant increase in participants' confidence in knowledge and application was observed. Data from 17 semistructured interviews indicated that the training delivery mode, content, and duration were feasible and well-accepted. Service providers reported making changes to their practice, including using more inclusive language, considering reasons for missed classes, and making changes to the physical space to improve safety. CONCLUSIONS: The online training was feasible, acceptable, and associated with improved confidence in applying TVIPA.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".