Comparative analysis of policies and programs to support families and children during COVID-19
Bibliographic record
Abstract
Abstract Background Policies and programs that promote positive social environments for young children and their families have the potential to improve early childhood development and long-term health. However, due to the community-wide public health measures implemented to reduce transmission of COVID-19, many families are experiencing health and socio-economic challenges and pre-existing supports and services may no longer be available. In this study, we compared the policies and programs countries have implemented to support maternal and child health during the first wave of COVID-19. Methods We compared the policies and programs implemented to support child health and well-being during the first wave of COVID-19 in Australia, Canada, the Netherlands, Singapore, the UK, and the USA. A grey literature review was performed to identify policies, announcements, and guidelines released from governmental and public health organizations within each country related to children, parents, families, early childhood development, adverse childhood experiences, child welfare, pre-school, or daycares. We also performed a manual search of government websites. Both provincial and federal government policies were included for Canada. Results The main policies identified were focused on prenatal care, well-baby visit and immunization schedules, financial supports, domestic violence and housing, childcare supports, child protective services, and food security. All of the included countries implemented some of these policies, but there was a large variation in the number, size, and barriers to access these supports. None of the countries implemented supports in all of the potential areas identified. Conclusion Political legacy and previous redistributive policies might have influenced the variation in policies and programs introduced by governments. As the COVID-19 pandemic continues, further opportunity for governments to implement supportive programs and policies for children and families exists.
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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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".