Bibliographic record
Abstract
Introduction: Measurements of child well-being in the military context through cross-national surveys must allow assessment of both (1) indicators for vulnerability and resilience in such children, and (2) factors leading to program success across the different NATO members. Methods: This review identifies psychometric properties (including validity, cross-cultural validation, sensitivity [SE], and specificity [SP] of each measure for various cutoffs for referral for psychiatric evaluation) as well as feasibility (cost-efficiency, time needed for filling in the questionnaire, language availability, and costs for its use). The measures included are four generic health-related quality-of-life measures (PedsQL 4.0, KIDSCREEN-52, DCGM-37, and KINDL-R) and four screening measures for mental health: the Achenbach System of Empirically Based Assessment (ASEBA), the Child Health Questionnaire (CHQ), the Pediatric Symptom Checklist (PSC), and the Strengths and Difficulties Questionnaire (SDQ). Results: High SE and SP values (0.70) for the screening instruments occurred in only 30%–55% of the studies reviewed. Cross-cultural validation and content validity are best covered by the KIDSCREEN-52, which is the dominant HRQOL instrument in Europe. The HRQOL instrument mostly used in the United States is the PedsQL. Discussion: Although there is no gold standard, the combination of a mental health screening instrument (the SDQ) with a HRQOL instrument (the KIDSCREEN-52), is recommended due to their complementary advantages on the evaluation criteria. Future comparability of items banks, such as those in the KIDSCREEN-52 and the PedsQL, is aimed for by the United States based PROMIS project.
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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 | 0.048 | 0.123 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.046 | 0.028 |
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".