Under-reporting of birth registrations in New South Wales, Australia
Bibliographic record
Abstract
Background: To determine the rates of birth registration over a five-year period in New South Wales (NSW) and explore the factors associated with the rate of registration.Methods: This is a cross-sectional study using linked population databases. The study population included all births of NSW residents in NSW between 2001 and 2005.Results: Birth registration rates in NSW were 82.66% in the year of birth, 93.19% in the first year, 94.02% in the second, 94.56% in the third and 95.08% in the fourth year after birth. The non-registration of births was mainly associated with such factors as neonatal and postneonatal death (adjusted OR = 3.84, 95% CI: 3.23-4.57); being Indigenous (adjusted OR = 3.26, 95% CI: 3.10-3.43); maternal age <25 or >39 years (adjusted OR = 2.81, 95% CI: 2.72-2.90); low birthweight (<2,500 grams) (adjusted OR = 1.79, 95% CI: 1.69-1.90); living in remote areas (adjusted OR = 1.57, 95% CI: 1.52-1.63); being born after the first quarter of year (adjusted OR = 1.08-1.56, 95% CI between 1.03-1.12 and 1.49-1.64); mother having more pregnancies (adjusted OR = 1.85-7.29, 95% CI between1.78-1.93 and 6.87-7.73). Mothers who were born overseas were more likely to register their births than those born in Australia (adjusted OR = 0.72, 95% CI: 0.69-0.75). Multiple births were more likely to be registered than singleton births (adjusted OR = 0.84, 95% CI: 0.76-0.92). About one-third of the non-registrations of births in NSW were explained by the risk factors. The reasons for the remaining non-registrations need to be investigated.Conclusion: Of birth in NSW, 4.92% were not registered by the fourth year after birth. © 2012 Xu et al.; licensee BioMed Central Ltd.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.001 | 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 teacher head, 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".