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Record W4224287104 · doi:10.32920/ihtp.v2i1.1622

Understanding the Health and Well-being of Canadian Black Children and Youth during the COVID-19 pandemic: A Review

2022· review· en· W4224287104 on OpenAlexaffvenueabout
Janet Kemei, Bukola Salami

Bibliographic record

VenueInternational Health Trends and Perspectives · 2022
Typereview
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychosocialPandemicPsychological interventionContext (archaeology)Social isolationSocial distancePsychologyCoronavirus disease 2019 (COVID-19)StressorPopulationHealth equityIsolation (microbiology)Public healthEnvironmental healthMedicineGerontologyDevelopmental psychologyPsychiatryGeographyNursingDisease

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has exacerbated health inequities and vulnerabilities in our society, with the Black population being disproportionately affected. As previous pandemics have resulted in an increase in adverse events to children and youth, we reviewed the literature to examine the impact of the COVID-19 pandemic on Black children and youth. We found Black children and youth experience psychosocial stressors related to uncertainties of the future. Differential gender effects related to COVID-19 are also apparent. Physical distancing related to the COVID-19 pandemic which resulted in differential impacts on physical activity levels in children. We also noted, increase levels of isolation may result in undetected child abuse. The review highlights the urgent need for multifaceted interventions that address disparities in social determinants of health and psychosocial needs of Black children and youth in Canada. Future research that addresses the effects of the COVID-19 pandemic on Black children and youth is needed to help create context-specific interventions.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.779
Threshold uncertainty score0.439

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.308
GPT teacher head0.479
Teacher spread0.171 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations3
Published2022
Admission routes3
Has abstractyes

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