Insights of Infantry Soldiers: A Qualitative Exploration of Psychological Resilience and Stress
Bibliographic record
Abstract
The Canadian Armed Forces (CAF) is currently struggling with a retention crisis. Within the CAF, The Canadian Army (CA) experiences the greatest attrition rates. Staffing shortages lead to an increase in job demands subsequently leading to greater stress, burnout, and turnover intentions. Psychological resilience has been found to buffer the negative effects of workplace stressors. There is a need to understand resilience within specific occupations to better inform resilience building interventions. This study aims to enhance knowledge of how infantry soldiers in the CA define resilience and what challenges they experience within the workplace that contribute to stress and how they cope with such stressors. A qualitative approach was used, with 14 semi-structured interviews conducted with CA personnel employed as infantry soldiers. Data were analyzed using a deductive content-analysis. Four themes emerged from the interviews: the nature of resilience, challenges of the profession, resilience strategies (attitudes), and resilience strategies (protective practices). The study provides unique insights into the experiences of infantry soldier’s and the mechanisms they employ to facilitate and maintain 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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".