Gathering voices and experiences of Australian military families: Developing family support resources
Bibliographic record
Abstract
Introduction: Young children from Australian Defence Force (ADF) families have experiences common to other military and Veteran families from other countries. Despite this, these families have unique cultural and contextual elements that need to be explored. Globally, research about how young children experience and understand parental deployment was collected from parents through proxy. A doctoral research study sought to privilege 2-to-5-year-old children's voices to ascertain what it was like to live in an ADF family. Subsequently, the project created age-appropriate and culturally appropriate resources to support these children in dealing with the stresses of military family life. Methods: Mosaic and narrative research approaches were employed to co-construct data and listen to 19 young children's voices. The study also listened to their parents and early childhood educators' voices as significant secondary sources of knowledge. Data collection tools included creative activities. Thematic analysis was employed. Results: Findings identified a dearth of age- and culturally appropriate resources to build children's abilities to make sense of their experiences. Children struggled with the ongoing nature of family transitions, often caused by parental deployment, training, and frequent family mobility. Additionally, children were subjected to various risk and protective factors. Discussion: Children's responses to parental absences, family mobility, and exposure to risk and protective factors aligned with international literature. The resource gap was partially addressed by co-creating free online research-based resources. These will be of interest to other family researchers and those who support children from military and Veteran 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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".