Equipping service providers: how social learning spaces enhance trauma- and violence-informed physical activity practices
Bibliographic record
Abstract
This study explored the effects of a social learning space on service providers’ knowledge, development, and implementation of trauma- and violence-informed physical activity (TVIPA) strategies for women. Using Wenger-Traynor’s social learning theory, combined with intersectional feminist theory, this study examined the experiences of 17 service providers delivering TVIPA programming to women in three geographic locations across Canada. Data were analysed using the value creation framework to interpret participant experiences in combination with value creation stories, which illustrated how social learning supported TVIPA knowledge and implementation. Findings indicate that the social learning space facilitated knowledge sharing, peer support, and reflective practice among service providers, ultimately strengthening their confidence and ability to adapt and implement TVIPA strategies. Key contributors to success included TVIPA training modules and structured collaborative activities that equipped participants with practical tools to address the systemic barriers women face when accessing physical activity. This research underscores the potential of structured social learning spaces as scalable and innovative tools for practice development, especially for service providers working with individuals from diverse backgrounds. These findings advocate for investment in social learning spaces to bolster TVIPA practices and other equity-focused health initiatives.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".