Bibliographic record
Abstract
Since 1990, 80,000 of Netherlands military personnel have participated in peacekeeping and peace-enforcing operations for the United Nations and NATO all over the world. In 2001, a Canadian study on suicide among Canadian veterans was published (Wong et al., Suicide and Life-Threatening Behavior, v31 n1, p103-112) in which the following question was asked: Are Canadian veterans more likely to commit suicide than other people their age? According to the Canadian authors, the answer is negative. However, in this meta-analysis, the authors try to answer this question in a different way. They examine several studies on suicide among Vietnam veterans that investigate the likelihood that such veterans will commit suicide, and the incidence of life-threatening behaviors, psycho-social problems, post-traumatic stress disorder (PTSD), depression, and guilt in this group. From this examination, the authors derive a model to explain why people commit suicide. The authors then describe and discuss three cases of suicide in the Netherlands Armed Forces, one of which occurred in a soldier who had returned from two tours in United Nations (UN) peacekeeping missions. They present a model for the prevention of suicide that is based on professional help. Veterans need to explore their feelings of depression and guilt, they need social support and care, and they need societal recognition of their service. The authors also present three cases of suicide in the U.S. Army in which soldiers killed their wives and then themselves, suicide data from a study of 15,000 Norwegians who participated in the UN Interim Forces in Lebanon (UNIFIL) mission, and suicide data from the U.S. Armed Forces and the Dutch Armed Forces. The data on the Dutch Armed Forces includes mortality data on Royal Netherlands Army, Navy, and Marine Corps veteran and active duty personnel. The authors also present the implications of this research for military mental health.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".