Critical Conversations: A restorative engagement initiative for people with lived experience of military sexual trauma
Bibliographic record
Abstract
Introduction: Military sexual trauma (MST) affects many Canadian Armed Forces (CAF) personnel and Veterans, with thousands of individuals reporting military sexual misconduct and resultant MST at some time in their career. The current study involved interviews with Veterans who were survivors of MST (people with lived experience [PWLE]) who met with CAF leadership to discuss the impact of MST on their lives. This provided a forum to share their stories with the goal of improving the institution's response to MST, ultimately influencing cultural change in the CAF. Methods: PWLE who participated in these meetings, dubbed Critical Conversations, with CAF leadership were interviewed. Directed content analysis and inductive analysis were used to explore the experiences of PWLE. Results: Eight people participated in the study and informed five themes: giving voice to those previously silenced, validation and empowerment, tackling guilt and shame, reclaiming identity and community, and the emotional cost of participation. Discussion: This study highlighted the range of emotions and insights experienced by those who engaged in the Critical Conversation meetings with CAF leadership on the effects of MST on their lives. The findings of this study explore a potential pathway of recovery for those who have survived MST during service with the CAF.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".