Cognitive performance improvement in Canadian Armed Forces personnel during deployment
Bibliographic record
Abstract
Introduction: Chronic stress can decrease resilience and diminish cognitive adaptability; thus, operational stressors related to military deployment can pose significant risks to cognitive functioning. Numerous studies have aimed to assess the effects of deployment on cognitive functioning on the basis of cognitive performance measures administered before and after deployment. However, to the best of our knowledge, no studies have measured neurocognitive performance of military personnel while they were deployed to a combat zone. Methods: Canadian Armed Forces military troops ( N = 85) were tested during pre-deployment training and during deployment in Afghanistan. At both time points, the participants completed a detailed demographic form and performed touch-screen neurocognitive tests using the Cambridge Neuropsychological Test Automated Battery (CANTAB). The CANTAB measurements included executive function (Attention Switching Task [AST] and Spatial Working Memory [SWM] test), decision making and response control (Stop Signal Task [SST]), and attention (Reaction Time [RTI] test). Two-tailed, paired t-tests were used to compare pre-deployment and deployment CANTAB results. Results: On average, all participants significantly improved their performance on all neurocognitive tests during deployment compared with pre-deployment. At both pre-deployment and deployment time points, the participants demonstrated excellent performance on the AST and RTI test and less-than-optimal performance on the SWM test and SST. Discussion: The influence of training, social factors, and emotional status, among many others, on cognitive adaptability should be taken into account to fully understand soldiers’ capability to improve and maintain high cognitive functioning during deployment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".