Examining the Relationship Between Mental, Physical, and Organizational Factors Associated With Attrition During Maritime Forces Training
Bibliographic record
Abstract
For infantry units of the Dutch Ministry of Defence, high attrition rates (varying from 42 to 68%) during initial training are a persisting problem. The reasons for this attrition are diverse. Having better insight into the causes of attrition is a prerequisite for implementing preventive measures. To achieve this, a monitoring assessment system was developed that integrated the effects of physical, mental, and organizational determinants on operational readiness. The aim of this study was to implement the monitoring tools and to establish the set of determinants that best predicted attrition during infantry training of new recruits. Eighty-five recruits were monitored over a 24-week infantry training course. Before the training, recruits were screened for medical, psychological, and physical wellness. During the monitoring phase, mental, physiological, and organizational indicants were obtained using an array of tools such as questionnaires, chest belt monitors (for heart rate, acceleration, and skin temperature measurements), and computerized tests (e.g., vigilance, long-term memory). Survival analyses were used to tease out the determinants of individual and grouped predictors of attrition. Nearly half the recruits (47%) failed the training. Attrition was predicted by both physiological and mental determinants. However, the organizational determinant "trainers' judgment" on the "recruits' military quality" dominated the physiological and mental determinants. It was concluded that the monitoring system was successfully implemented during infantry training, and that the survival analysis method emphasized on single effects and interactions between the different determinants. Based on the current findings, we recommend several steps to successfully implement a monitoring method in settings with high demands.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".