Combatting job stress: Integrating healthy workplace and stress models to support military member well-being
Bibliographic record
Abstract
Introduction: Supporting worker well-being and reducing stress is important in all jobs, and even more so in a military environment. Military members may be subjected to a unique set of stressors and demands (e.g., deployments, combat), as well as demands that are typical across occupations (e.g., work overload), which are associated with significant organizational (e.g., recruitment, retention) and individual (e.g., PTSD, burnout) outcomes. Although less frequently studied, military occupations also are associated with positive work (e.g., skill development) and life (e.g., resilience, sense of purpose) outcomes. Even though identifying mechanisms to support military member health can be daunting (especially given the variety of stress and well-being models), identifying potential points of intervention is vital to understanding worker health. Methods: The authors identified and reviewed seminal stress, well-being, healthy workplace, and intervention models and theories to integrate both unique and common work-related factors that were pertinent to a military population. Results: The authors summarized these key models and theories and developed a military well-being framework including key intervention points to help militaries actively address member well-being from multiple points. Discussion: The goal of the study was to provide an updated, workable framework to address well-being within a military population, while emphasizing key job, interpersonal, and organizational demands/resources. It affords a unique perspective and tool to support future research and practice by linking demands/resources and well-being to primary, secondary, and tertiary workplace interventions.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".