Purpose After Service Through Sport: Randomized Controlled Trial
Bibliographic record
Abstract
Background and Context: The transition to civilian life can bring about substantive challenges for military veterans. This was highlighted in a prominent 2014 report by Canada’s Senate Subcommittee on Veterans affairs (Dallaire & Wells, 2014). Particular challenges include a loss of identity, feelings of loneliness and isolation (Wilson et al., 2018), difficulties with finding and retaining employment (Dallaire & Wells, 2014), substance misuse (Tam et al., 2020), and problems adjusting to very different work and cultural environments. A growing body of scientific evidence also indicates that military veterans are at notable risk of depleted mental health (e.g., depression, suicide ideation) (Shields et al., 2016). On leaving the military, many veterans are difficult to reach, while others are often reluctant to access support services (Shields et al., 2016), with men in particular reluctant to seek help from professionals (McCreary, 2019). A review of military veterans’ programs, conducted by the Movember Foundation (McCreary, 2019), revealed that much of the evidence to date related to mental health programs for military veterans has been of low quality (i.e., poor validity evidence, weak research designs), with limited evidence for program effectiveness. In this study, we will broaden the evaluation (e.g., assess its impact on mental and physical health) and reach (e.g. extend to other Canadian cities) of our recently developed pilot program, Purpose After Service through Sport (PASS), which was completed in Vancouver in 2020 and assessed for its feasibility and acceptability (see Waldhauser et al., 2021). The results from this pilot study revealed that the program was well-received (by both military leadership and veterans themselves), fostered a sense of social connectivity among veterans, resulted in (self-reported) physical and psychological health benefits, and displayed considerable potential for scale up and evaluation using a randomized controlled trial (RCT) design. The project directly addresses limitations revealed in the Movember Foundation Report (McCreary, 2019) and draws from four established and well validated lines of research. First, there is compelling evidence for the role of physical activity in buffering against mental health deficits (e.g., depression, anxiety, PTSD) and promoting psychological well-being (Kvam et al., 2016; McDowell et al., 2019; Rosenbaum et al., 2015). Second, group-based initiatives that foster social connectivity have been found to result in significant improvements in quality of life, self-esteem, and cognitive health, as well as reductions in anxiety, depression, and stress (Steffens et al., 2019). Third, PASS represents the first program, that we are aware of, to support veteran well-being that takes a gendered lens. As McCreary (2019) noted in the Movember report, a major limitation of existing programs is that “No-one is applying a gendered lens to the programs they develop and implement”, p. 8). Finally, our project will use a randomized trial design to comprehensively examine the efficacy of the program to support military veteran well-being. When taken together, our PASS Randomized Trial draws from the empirical evidence related to (a) the mental health benefits of regular physical activity, (b) benefits of group-based programs that foster social connectivity, (c) the importance of, and potential derived from, developing and implementing gender-sensitized programs, and (d) our initial pilot study.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.002 |
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".