Jurisdictional, socioeconomic and gender inequalities in child health and development: analysis of a national census of 5-year-olds in Australia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: Early child development may have important consequences for inequalities in health and well-being. This paper explores population level patterns of child development across Australian jurisdictions, considering socioeconomic and demographic characteristics. DESIGN: Census of child development across Australia. SETTING AND PARTICIPANTS: Teachers complete a developmental checklist, the Australian Early Development Index (AEDI), for all children in their first year of full-time schooling. Between May and July 2009, the AEDI was collected by 14 628 teachers in primary schools (government and non-government) across Australia, providing information on 261 147 children (approximately 97.5% of the estimated 5-year-old population). OUTCOME MEASURES: Level of developmental vulnerability in Australian children for five developmental domains: physical well-being, social competence, emotional maturity, language and cognitive skills and communication skills and general knowledge. RESULTS: The results show demographic and socioeconomic inequalities in child development as well as within and between jurisdiction inequalities. The magnitude of the overall level of inequality in child development and the impact of covariates varies considerably both between and within jurisdiction by sex. For example, the difference in overall developmental vulnerability between the best-performing and worst-performing jurisdiction is 12.5% for males and 7.1% for females. Levels of absolute social inequality within jurisdictions range from 8.2% for females to 12.7% for males. CONCLUSIONS: The different mix of universal and targeted services provided within jurisdictions from pregnancy to age 5 may contribute to inequality across the country. These results illustrate the potential utility of a developmental census to shed light on the impact of differences in universal and targeted services to support child development by school entry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it