The impact of parental military service on child well-being
Bibliographic record
Abstract
Introduction: The aim of this review is to evaluate the literature on the association between parental military-related factors and child well-being. Methods: We conducted a literature search for research published from 2000–2017 from NATO and Partnership for Peace (PfP) countries in the English language only. Eligible studies were those that included topics of military personnel and children’s well-being; papers that included child maltreatment/abuse were excluded. Search databases included Embase, Medline, PsycINFO, ScienceDirect, Web of Science, Google Scholar, and PubMed. Results: Thirty-six predominantly United States (US)-based studies were included in the review: 27 of cross-sectional study design, 4 longitudinal, and 5 retrospective cohort studies. Discussion: The parental military-specific factors that affect child well-being are cumulative deployment months, frequent relocation, and factors related to relocation such as expanded household responsibility, disrupted daily routines, academic interruption, and disruption to social networks. These factors are associated with military children having higher levels of emotional and behavioural difficulties – such as symptoms of depression – than their civilian counterparts. Limitations of the review include the large proportion of studies with a cross-sectional design, as well as studies with small sample sizes. Indications for future research include looking at children from dual military families and the use of longitudinal study designs.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".