Reducing Veterans’ Symptoms of Depression, Anxiety, Stress, and Posttraumatic Stress, and Enhancing Engagement in Occupations with SCUBA Diving and Occupational Therapy
Bibliographic record
Abstract
Veterans face significant occupational and mental health challenges. This article explores whether an occupational therapy program in combination with SCUBA diving can enhance occupational performance and mental health outcomes for veterans more than SCUBA diving alone. A two-group random assignment pretest–posttest design was implemented to compare outcomes across two groups. Veterans in Group 1 (n = 7) completed SCUBA diving while those in Group 2 (n = 8) completed SCUBA diving in combination with occupational therapy. Outcomes were measured using the Canadian Occupational Performance Measure, PTSD Checklist for DSM-5, and the Depression, Anxiety and Stress Scale. Both groups participated in a semi-structured focus group post-participation. Additionally, those in Group 2 engaged in follow-up phone interviews. Trial registration number: NCT03928392. All participants significantly improved their occupational performance and satisfaction with performance, and reduced symptoms of PTSD, depression and stress. Those in Group 2 also experienced significant reductions in anxiety levels and reported applying learned interventions to daily life. Participating in SCUBA diving may contribute to reduced symptoms of PTSD, depression and stress among veterans. Engaging in occupational therapy may additionally contribute to reduced symptoms of anxiety and aid in therapeutic application of interventions to daily life.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".