Brain Bootcamp: Pre–post comparison findings of an integrated behavioural health intervention for military members with reduced executive cognitive functioning
Bibliographic record
Abstract
Introduction: Canadian Armed Forces (CAF) Service members (SMs) experience higher rates of mild traumatic brain injuries (mTBIs) and psychosocial risk factors such as mental health diagnoses, sleep disturbances, alcohol consumption, and post-concussion symptoms than Canadian civilians. Associated challenges with executive cognitive functioning (ECF) can significantly impede their performance, engagement, and deployability. To address challenges with ECF, an occupational therapist providing rehabilitation services to CAF SMs created and delivered Brain Bootcamp – an integrated behavioural health intervention for CAF SMs who sustained an mTBI or more serious traumatic brain injury (TBI) and had reduced ECF. Although anecdotal post-intervention feedback is favourable, Brain Bootcamp’s impact on ECF in individuals with mTBI or TBI, mental health diagnoses, or both has yet to be determined. This study aimed to determine whether Brain Bootcamp improves cognitive performance, reduces mTBI- and TBI-related symptoms, and increases external aid utilization among CAF SMs with reduced ECF. Methods: We conducted a quasi-experimental study of clinical outcomes collected from 55 participants who participated in Brain Bootcamp. Measures used to determine changes in client ECF before and after the intervention included the Montreal Cognitive Assessment, Rivermead Post-Concussion Symptom Questionnaire, and External Aids Utilization Survey. Results: Statistically significant changes pre- and post-intervention were observed, including improved cognitive performance, reduced self-reported mTBI or TBI symptoms, and increased external aid utilization. Discussion: Brain Bootcamp may have a positive effect on ECF. Such improvements can enable CAF SMs to be operationally ready for military service and have greater overall well-being. Brain Bootcamp appears to be a promising ECF-enhancing intervention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".