An Investigation of the Character Strengths and Resilience of Future Military Leaders
Bibliographic record
Abstract
Introduction: The importance of both character and resilience for critical occupations (military, emergency medicine, first responders, and correctional officers) has been emphasized at the highest levels of military leadership. No studies to date have examined the relationship between character strengths and resilience within military populations. The purpose of this study was to evaluate the perceived importance of character strengths for Canadian military cadet success, the top strengths endorsed by cadets, and, in a subset of cadets, the relationships among core strengths and resilience. In line with previous research on character strengths in military populations, we predicted that bravery, honesty, perseverance, and teamwork might be included in the five most frequent signature strengths. Methods: A total of 360 Naval/Officer Cadets from a Canadian Military College were invited to participate in a study during two training sessions. Participants (n = 153) first completed a survey comprised of a resilience measure and demographic items. Then, one month later, students (n=134) were asked to complete a Values in Action (VIA) character strengths profile, and a survey with questions related to character strengths (their personal top-five character strengths, and strengths they believed were important for military-related stressors and leadership, academic success, resilience, and completion of military challenge). We were only able to match responses for a subset of participants, allowing a final sample of 94 participants. Results: Findings indicated that military cadets consider perseverance, judgment, teamwork, perspective, and self-regulation to be most critical for bouncing back from stressors. However, in line with our predictions, the most frequently endorsed strengths that characterized cadets were bravery, honesty, and perseverance. Finally, perseverance (p = .029), bravery (p = .01), and humor = .01) were positively correlated with cadet resilience, while endorsement of love was negatively correlated with resilience (p = .002). Conclusion: Focus on character strengths in military cadets can enhance academic and physical performance. Resilience assessment could be important for the purposes of military selection, performance, and well-being. Our findings indicate perseverance, bravery, and humor in particular might be relevant indicators of cadet resilience.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".