A Family Affair: Growth within injured Veterans and their support network
Bibliographic record
Abstract
This qualitative phenomenological study explored the potential for growth within injured or ill Canadian Armed Forces (CAF) Veterans, as well as members of their support networks. 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, Wadey, & Diss, 2015). Guided by the work of Roy-Davis, Wadey, and Evans (2016) and through the lens of Organismic Valuing Theory (Joseph & Linley, 2005), this study sought out a context-specific understanding of the concept of growth within CAF. An additional focus was on the impact of veterans’ stress and/or trauma on support members and the potential that they may experience positive changes following indirect exposure to a loved one’s trauma (Dekel, Levin, & Solomon, 2015). Organismic Valuing Theory was explored as a potential theory to understand growth within the CAF context.\nThis research expanded on the sport injury growth research done by Roy-Davis et al. (2016) and caregiver growth research (Leith, Jewel & Stein, 2018; Mavandadi et al., 2014; Savage & Bailey, 2004). Semi-structured interviews were conducted with 7 participants including 1 dyad, 1 triad, a single Veteran, and a single support person. Through the interviews six higher order themes emerged: 1) relationships, 2) the power of the uniform, 3) new perspectives, 4) a complex support paradox, 5) letting go and moving forward, and 6) The Caregiver Experience. As highlighted by the participants, support members, particularly in the CAF, are key resources in the recovery and growth process but are often overlooked. A timely subject, this research benefits both Veterans and their support persons struggling following stressful and traumatic situations.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".