A family affair: Growth within injured veterans and their support networks
Bibliographic record
Abstract
The present study explored the potential for growth within an often-overlooked group of injured or ill Canadian Armed Forces (CAF) Veterans, and their support networks (spouse, sibling). Growth is most commonly understood as perceived positive changes experienced by individuals following a stressor which propel them to a higher level of functioning (Salim et al., 2015). The study sought to develop a unique, context-specific understanding of growth within the CAF. Additionally, the study focused on the potential impact of stress and trauma on support members and subsequent positive change experiences (secondary growth) following indirect exposure to a loved one's trauma (Dekel et al., 2015). The present study was guided by sport injury growth research (Roy-Davis et al., 2016) and caregiver growth research (Leith et al., 2018; Mavandadi et al., 2014; Savage & Bailey, 2004). Semi-structured interviews were conducted with 7 participants; 1 dyad, 1 triad, a single veteran, and a single support person. Six higher order themes emerged: relationships, power of the uniform, new perspectives, complex support paradox, letting go and moving forward, and the caregiver experience. Support members in the CAF context were highlighted as key pieces in the recovery and growth process but are often overlooked. With the evident lack of support highlighted by Veterans and support members, the present study provides a crucial first step to addressing support issues and developing strategies to support the CAF population following trauma. This presentation will focus on emergent themes and specific implications for the CAF population and their support members.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".