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Record W4411025097 · doi:10.61959/gcfd3756e

COVID-19 IMPACTS: Youth Well-being in Canada

2020· report· en· W4411025097 on OpenAlexaboutno aff
Edward Ng, Nadine Badets

Bibliographic record

Venuenot available
Typereport
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PandemicGeographyVirologyBiologyMedicineOutbreakInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Spring and summer 2020 have been a unique time for children and youth in Canada, as families across the country have been adapting their routines, plans and activities in light of physical distancing and other public health measures in place to prevent the spread of COVID-19. With schools closing their doors and moving online in the spring, nearly 5 million children and youth across the country were no longer spending their days near their friends and peers as they and their families adapted and reacted to the evolving situation.1 While surveys have shown that most youth are adhering to public health measures and have demonstrated resilience, this disruption had been difficult for many. In a poll conducted by UNICEF Canada, the toughest aspects cited by youth have been not being able to leave the house, go to school and spend time with friends.2 This matters for the well-being of children and youth, since research shows that social interaction is fundamental in youth development, as positive influences by and among peers are important for students’ academic achievement and success later in life.3

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)0.001
metaresearch head score (Gemma)0.002
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: Other · Consensus signal: none
Teacher disagreement score0.051
Threshold uncertainty score0.369

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0050.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.108
GPT teacher head0.430
Teacher spread0.322 · 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
GenreOther

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
Published2020
Admission routes1
Has abstractyes

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