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Record W3046944047 · doi:10.1093/pch/pxaa086

Supporting children and youth during the COVID-19 pandemic and beyond: A rights-centred approach

2020· article· en· W3046944047 on OpenAlexaffabout
Shazeen Suleman, Yasmine Ratnani, Katrina Stockley, Radha Jetty, Katharine Smart, Susan Bennett, Sarah Gander, Christine Loock

Bibliographic record

VenuePaediatrics & Child Health · 2020
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsMemorial University of NewfoundlandChildren's Hospital of Eastern OntarioYukon UniversityUniversity of OttawaUniversity of British ColumbiaBC Children's HospitalDalhousie UniversityUniversity of TorontoUniversité de MontréalSt. Michael's Hospital
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Political scienceAction (physics)Convention on the Rights of the ChildConventionEconomic growthCriminologyMedicineHuman rightsPsychologyEconomicsLawDisease

Abstract

fetched live from OpenAlex

The COVID-19 pandemic is an unprecedented global crisis, affecting millions globally and in Canada. While efforts to limit the spread of the infection and 'flatten the curve' may buffer children and youth from acute illness, these public health measures may worsen existing inequities for those living on the margins of society. In this commentary, we highlight current and potential long-term impacts of COVID-19 on children and youth centring on the UN Convention of the Rights of the Child (UNCRC), with special attention to the accumulated toxic stress for those in difficult social circumstances. By taking responsive action, providers can promote optimal child and youth health and well-being, now and in the future, through adopting social history screening, flexible care models, a child/youth-centred approach to "essential" services, and continual advocacy for the rights of children and youth.

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)0.019
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.035
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0080.026
Scholarly communication0.0110.013
Open science0.0050.011
Research integrity0.0250.038
Insufficient payload (model declined to judge)0.0050.001

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 head0.037
GPT teacher head0.304
Teacher spread0.266 · 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 designTheoretical or conceptual
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

Citations17
Published2020
Admission routes2
Has abstractyes

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