Measuring Resilience to Operational Stress in Canadian Armed Forces Personnel
Bibliographic record
Abstract
Adaptability to stress is governed by innate resilience, comprised of complex neuroendocrine and immune mechanisms alongside inherited or learned behavioral traits. Based on their capacity to adapt, some people thrive in stressful situations, whereas others experience maladaptation. In our study, we used state-of-the-art tools to assess the resilience level in individuals, as well as their susceptibility to developing military stress-induced behavioral and cognitive deficits. To address this complex question, we tested Canadian Armed Forces (CAF) personnel in three distinct stress environments (baselines): during predeployment training, deployment in Afghanistan, and readjustment upon return to Canada. Our comprehensive outcome measures included psychometric tests, saliva biomarkers, and computerized cognitive tests that used the Cambridge Neuropsychological Automated Test Battery. Participants were categorized based on initial biomarker measurements as being at low-, moderate-, or high stress-maladaptation risk. Biomarkers showed significant changes (ds = 0.56 to 2.44) between baselines, calculated as "delta" changes. Participants at low stress-maladaptation risk demonstrated minimal changes, whereas those at high stress-maladaptation risk showed significant biomarker variations. The psychometric patterns and cognitive functions were likewise affected across baselines, suggesting that the panel of saliva stress biomarkers could be a useful tool for determining the risk of stress maladaptation that can cause psychological and cognitive decline.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".