Enhancing Resilience in Canadian Military Families and Communities: A Qualitative Analysis of the Reaching In… Reaching Out and Bounce Back and Thrive! Resiliency Skills Training Programs
Bibliographic record
Abstract
Introduction: A new vision of resilience and well-being for Canadian military service members (SMs), Veterans and their families has been championed by the Canadian Armed Forces (CAF) and Veterans Affairs Canada (VAC). Operationalizing this vision, which aims to support those who serve/have served and their families as they navigate life during and post-service, requires the support of service providers (SPs). Training SPs to deliver complementary resilience-training programs Reaching In… Reaching Out (RIRO; for adults working with parents of young children) and Bounce Back and Thrive! (BBT; for parents of children aged 0–8 years of age) may support this vision. Objective: To assess the appropriateness of RIRO/BBT trainer training for SPs, and RIRO and BBT resilience-training for military populations and families. Methods: This qualitative descriptive study involved the delivery of RIRO/BBT trainer training to SPs (n = 20), followed by focus groups (n = 6) with SPs and organisational leaders (n = 4). Focus groups were recorded, and data were transcribed and thematically-analysed. Results: Several themes emerged: (1) RIRO/BBT trainer training enabled SPs to model resilience and deliver the resilience-training programs, (2) training was appropriate and adaptable for the CAF and SMs/CMFs, and (3) training could support the development of resilient communities. Discussion: RIRO/BBT trainer training and RIRO and BBT resilience-training programs use a holistic, integrated, experiential, and community approach to resilience-building and align with CAF and VAC initiatives. Once contextualised, such programs could support resilience-building in the military context.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.034 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".