MétaCan
Menu
Back to cohort
Record W3133857575 · doi:10.1101/2021.03.04.21252943

Comparative analysis of policies and programs to support families and children during COVID-19

2021· preprint· en· W3133857575 on OpenAlexaffabout
Joanne Kearon, Mark Cachia, Sarah Carsley, Meta van den Heuvel, Jessica Hopkins

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoPublic Health OntarioMcMaster UniversityImpact
Fundersnot available
KeywordsGovernment (linguistics)Economic growthPublic healthPublic policyPolitical scienceWelfarePoliticsBusinessMedicineNursingEconomics

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.058
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.300

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.058
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.012
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.090
GPT teacher head0.442
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same venuemedRxivSame topicChild and Adolescent HealthFrench-language works237,207