Longitudinal analysis of psychological resilience and mental health in Canadian military personnel returning from overseas deployment.
Bibliographic record
Abstract
The relationship between exposure to combat stressors and poorer postdeployment health is well documented. Still, some individuals are more psychologically resilient to such outcomes than others. Researchers have sought to identify the factors that contribute to resilience in order to inform resilience-building interventions. The present study assessed the criterion validity of a model of psychological resilience composed of various intrapersonal and interpersonal variables for predicting mental health among Canadian Forces (CF) members returning from overseas deployment. Participants included 1,584 male CF members who were deployed in support of the mission in Afghanistan between 2008 and 2010. Data on combat experiences and mental health collected through routine postdeployment screening were linked with historical data on the intrapersonal and interpersonal variables from the model. The direct and moderating effects of these variables were assessed using multiple linear regression analyses. Analyses revealed direct effects of only some intrapersonal and interpersonal resilience variables, and provided limited support for moderating effects. Specifically, results emphasized the protective nature of conscientiousness, emotional stability, and positive social interactions. However, other variables demonstrated unexpected negative associations with postdeployment mental health (e.g., positive affect and affectionate social support). Ultimately, results highlight the complexities of resilience, the limitations of previous cross-sectional research on resilience, and potential targets for resilience-building interventions. Additional longitudinal research on the stability of resilience is recommended to build a better understanding of how resilience processes may change over time and contribute to mental health after adverse experiences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".