The family experience of the Invictus Games through a work family conflict lens: An interpretative phenomenological analysis
Bibliographic record
Abstract
Abstract The Invictus Games are an international adaptive sports competition for wounded, injured, and/or ill military members and veterans. Essential to the Games are the families and friends of the competitors, who are involved from selection to post‐game. Though families are integral, their experiences are underexplored. Little research to date has focused on the families, and even less research has been conducted using a family science lens. Work–family conflict theory provides a way to examine the stressors and support that underlie the family experience. Using interpretative phenomenological analysis with a work–family conflict lens, this project explores the family members' perspectives of being at the Invictus Games. Participants were past family and friends from Canada ( n = 5), the UK ( n = 1) and Australia ( n = 1). The results display that the Invictus Games can be a very positive experience for both the individual family members as well as the family as a whole. The results challenge the existing idea that participating in sports automatically results in work–family conflict. This project highlights the importance of family inclusion within and throughout military and veteran adaptive sport competitions. As the Invictus Games continue to grow, it is important to be intentional about how families are supported.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".