What factors contribute to positive early childhood health and development in Australian Aboriginal children? Protocol for a population-based cohort study using linked administrative data (The Seeding Success Study)
Bibliographic record
Abstract
INTRODUCTION: Australian Aboriginal children are more likely than non-Aboriginal children to have developmental vulnerability at school entry that tracks through to poorer literacy and numeracy outcomes and multiple social and health disadvantages in later life. Empirical evidence identifying the key drivers of positive early childhood development in Aboriginal children, and supportive features of local communities and early childhood service provision, are lacking. METHODS AND ANALYSIS: The study population will be identified via linkage of Australian Early Development Census data to perinatal and birth registration data sets. It will include an almost complete population of children who started their first year of full-time school in New South Wales (NSW), Australia, in 2009 and 2012. Early childhood health and development trajectories for these children will be constructed via linkage to a range of administrative data sets relating to birth outcomes, congenital conditions, hospital admissions, emergency department presentations, receipt of ambulatory mental healthcare services, use of general practitioner services, contact with child protection and out-of-home care services, receipt of income assistance and fact of death. Using multilevel modelling techniques, we will quantify the contributions of individual-level and area-level factors to variation in early childhood development outcomes in Aboriginal and non-Aboriginal children. Additionally, we will evaluate the impact of two government programmes that aim to address early childhood disadvantage, the NSW Aboriginal Maternal and Infant Health Service and the Brighter Futures Program. These evaluations will use propensity score matching methods and multilevel modelling. ETHICS AND DISSEMINATION: Ethical approval has been obtained for this study. Dissemination mechanisms include engagement of stakeholders (including representatives from Aboriginal community controlled organisations, policy agencies, service providers) through a reference group, and writing of summary reports for policy and community audiences in parallel with scientific papers.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".