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Record W4285794016 · doi:10.1186/s12889-022-13344-0

Early (years) reactions: comparative analysis of early childhood policies and programs during the first wave of the COVID-19 pandemic

2022· article· en· W4285794016 on OpenAlexaffabout
Joanne Kearon, Sarah Carsley, Meta van den Heuvel, Jessica Hopkins

Bibliographic record

VenueBMC Public Health · 2022
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Impact on Reproduction
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoPublic Health OntarioMcMaster UniversityImpact
Fundersnot available
KeywordsMedicineBiostatisticsPandemicCoronavirus disease 2019 (COVID-19)Public healthEpidemiology2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)VirologyEnvironmental healthPediatricsFamily medicineInfectious disease (medical specialty)NursingOutbreakPathologyDisease

Abstract

fetched live from OpenAlex

BACKGROUND: During the first wave of COVID-19 there was little evidence to guide appropriate child and family programs and policy supports. METHODS: We compared policies and programs implemented to support early child health and well-being during the first wave of COVID-19 in Australia, Canada, the Netherlands, Singapore, the UK, and the USA. Program and policy themes were focused on prenatal care, well-baby visits and immunization schedules, financial supports, domestic violence and housing, childcare supports, child protective services, and food security. RESULTS: Significant heterogeneity in implementation of OECD-recommended policy responses was found with all of the included countries implementing some of these policies, but no country implementing supports in all of the potential areas. CONCLUSIONS: This analysis gives insight into initial government reactions to support children and families, and opportunities for governments to implement further supportive programs and policies during the current pandemic and future emergencies.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.037
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.126
GPT teacher head0.379
Teacher spread0.253 · 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 teacher head, 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

Citations1
Published2022
Admission routes2
Has abstractyes

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