Having babies in times of uncertainty: first results of the impact of COVID-19 on the number of babies born in Australia
Bibliographic record
Abstract
Background There has been considerable speculation on whether the COVID-19 pandemic had an effect on childbearing behaviour. Based on the experience of other social and economic disruptions, many researchers suggested that births would decline, while others argued that there could be a positive effect. Aims This paper considers the uncertainties associated with the impacts of COVID-19, particularly the relationship between the timing of COVID-19 events and subsequent births. Data and methods Publicly available birth data from birth registers, perinatal databases, and public hospital data were compiled and analysed to document changes in numbers and patterns of recorded births during 2020 and 2021. Results Births declined in 2020 but then rebounded in 2021. Quarterly birth data from New South Wales and Western Australia suggest that the sharpest drop in conceptions occurred in the January-March 2020 quarter. This coincided with the period when the pandemic was first taking off and when uncertainty about the future was at its highest. Conclusions The uncertainty associated with the onset of the COVID-19 pandemic had a noticeable impact on births in 2020. It also shows, where data is available, that this impact was relatively short-lived, and births rebounded in 2021. We note that data is still sparse for Victoria, a state which was substantially more affected by lockdowns.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".