Children from military families: looking through a transnational lens
Bibliographic record
Abstract
Introduction: The purpose of the NATO Human Factors Medicine Research Task Group-258 (HFM RTG-258) was to develop a theoretical model of well-being for children from military families so as to assist militaries and service providers in identifying the most effective supports for military families and their children. Methods and Results: Building on existing models of children’s well-being and the socio-political context of NATO and Partnership for Peace (PfP) nations, as well as the unique challenges of military life, we developed the health, education, legal, material, and social (HELMS) model, a universal framework for the well-being of children in military families. The model is specific to the unique aspects shared by military families and children while allowing for the differences and similarities of military families across different nations. Our aim is also to initiate a dialogue beyond NATO and PfP nations, and our task group serves as a forum for active collaboration on surveys and metrics to guide current and future work. Discussion: The outcome of this NATO task group will not only benefit NATO military families and its partner nations, policy makers, and military organizations internationally, but it will also help service providers identify the most effective ways of providing support to military families and their children.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".