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Record W6940292081 · doi:10.6084/m9.figshare.c.6103101

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

2022· other· en· W6940292081 on OpenAlexaffabout

Bibliographic record

VenueFigshare · 2022
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of TorontoPublic Health OntarioMcMaster University
Fundersnot available
KeywordsPandemicGovernment (linguistics)Early childhoodPublic policyPolicy analysisCoronavirus disease 2019 (COVID-19)Public health

Abstract

fetched live from OpenAlex

Abstract 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 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)0.005
metaresearch head score (Gemma)0.014
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 score0.031
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
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 head0.055
GPT teacher head0.258
Teacher spread0.203 · 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
Published2022
Admission routes2
Has abstractyes

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