Development of a model of well-being for children from military families in NATO countries
Bibliographic record
Abstract
Introduction: This article describes the development of a model of well-being for children from military families in NATO countries. Methods: The development comprised seven phases: (1) a review of the participating NATO countries’ literature (current theory and research); (2) an outline of the key elements of a model of child well-being; (3) the identification of potential indicators of child well-being; (4) the identification of the components and dimensions of child well-being; (5) a review and refinement of the model; (6) the inclusion of the military factors that influence child well-being; and (7) the development of a definition for child well-being. Results: Thematic analysis was used to identify the well-being indicators, dimensions, and components. The process was informed by the subject matter expertise of the NATO Human Factors Medicine Research Task Group-258 (NATO HFM RTG-258), the civilian literature, and military factors associated with child well-being. Discussion: Central to the developed child well-being model are five dimensions of child well-being: health, education, legal, material, and social (HELMS). This model is closely aligned with well-established models and measures of well-being – Bronfenbrenner’s bioecological model of human development, Minkkinen’s structural model of child well-being, and the Organisation for Economic Co-operation and Development’s (OECD) measures of child-well-being. The proposed model takes into consideration relevant military factors that influence the well-being of children in military families.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".