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Record W4408658547 · doi:10.35502/jcswb.413

Lessons to learn: Strategies to sustain a restorative program for survivors of military sexual trauma

2025· article· en· W4408658547 on OpenAlexaffvenueabout
Linna Tam‐Seto, Lisa Garland-Baird, Nicholas Held, Alexandra Heber, Lori Buchart, Ashley Ibbotson, Sarah Lade, Heather Millman, Andrea Brown, Bibora Imre-Millei, Marguerite Samplonius, Christina Chrysler, Margaret C. McKinnon

Bibliographic record

VenueJournal of Community Safety and Well-Being · 2025
Typearticle
Languageen
FieldPsychology
TopicCounseling Practices and Supervision
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster UniversityVeterans Affairs CanadaUniversity of Toronto
Fundersnot available
KeywordsPsychologyClinical psychology

Abstract

fetched live from OpenAlex

Since the release of the Arbour Report in 2015, efforts have been made within the Canadian Armed Forces (CAF) and Department of National Defence (DND) to develop and employ programs and services to support people with lived experiences (PWLE) of military sexual trauma (MST). Based on a pilot initiative, the current paper describes some strategies that may contribute to the success of programs aimed at reconciliation and recovery for both people who have been directly harmed and the institution as a whole. These strategies are grounded in the specific context of those reconciling the trauma from MST and consistent with the wider landscape of research and best practices for restorative programs. Strategies include creating a sustainability plan; enhancing planning and preparation; strengthening meeting frameworks; and developing post-meeting strategies. Many of these strategies are aimed at addressing institutional betrayal and healing for survivors and representatives of the organization.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.389
Teacher spread0.355 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes3
Has abstractyes

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