Evaluating an expressive writing program in supporting military women’s transition to civilian life
Bibliographic record
Abstract
Expressive writing programs provide opportunities to engage in reflective writing centred on lived experience. Research shows the benefits of expressive writing for Veterans reintegrating into civilian life; however, there is a need for programs that are specific to women, in light of their gendered experiences in the military. The goal of this study was to articulate the program theory and examine preliminary evidence of outcomes for a new peer-led expressive writing program for women who have served in the Canadian military. An outcome harvesting approach was used to collect data through surveys and interviews with participants and facilitators (N = 7). Participants spoke of experiencing the program as a safe space because of the self-directed participation of the writing and sharing process. Many women related they felt their experiences were affirmed through the program's focus on positive feedback. This was further noted in the fact that writing in the program was treated as fiction, which created a "safe distance" and in turn allowed for deep connection between participants. Positive changes were found for the following outcomes: connection with others, well-being, creativity, empowerment of voice, and willingness to share.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".