A Longitudinal Pilot Study of Resilience in Canadian Military Personnel
Bibliographic record
Abstract
Research on psychological resilience is important for occupations involving routine exposure to trauma or critical events. Such research can allow for the identification of factors to target in training, education and intervention programs, as well as groups that may be at higher risk for mental health problems. Although efforts have been made to determine the individual characteristics that contribute to positive outcomes under stress, little is known about whether such characteristics are stable over time or how stressful events can impact psychological resilience in high-risk occupations such as military service. Following a review of the evidence on variations in resilience over time, results of a pilot study of Canadian Armed Forces personnel are presented in which differences in resilience characteristics were examined from military recruitment to several years after enrollment. While there was little change in resilience characteristics over time on average, there was considerable individual variation, with some individuals showing marked improvement and others showing marked deterioration in resilience characteristics. At both time points, individuals who had been deployed showed greater resilience characteristics than those who had never been deployed. Implications for the promotion of psychological resilience in military populations and personnel employed in other high-risk occupations are discussed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".