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An ethical analysis of the impacts of the COVID-19 pandemic on the health of children and adolescents

2022· article· en· W4285143456 on OpenAlexaff
Raíssa Passos dos Santos, Eliane Tatsch Neves, Ivone Evangelista Cabral, Sydney Campbell, Franco A. Carnevale

Bibliographic record

VenueEscola Anna Nery · 2022
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsInstitute for Work & HealthUniversity of TorontoMcGill University
Fundersnot available
KeywordsPandemicAction (physics)Coronavirus disease 2019 (COVID-19)PsychologyPolitical scienceDimension (graph theory)2019-20 coronavirus outbreakCriminologyMedicineDiseaseVirology

Abstract

fetched live from OpenAlex

ABSTRACT The COVID-19 pandemic has impacted the lives of children and adolescents around the world. Hence, this study aimed to examine how the pandemic has impacted children and adolescents in Brazil through an ethical analysis. An interpretive analysis of Brazilian research on child and adolescent health during the pandemic was conducted. Recognizing this ethical dimension is pivotal to shedding more light on how responses to crisis situations, such as the current situation of the COVID-19 pandemic, can be shaped and where the priorities for action are according to all interested parties, situating the child between these parts of interest. Our analysis highlighted both direct and indirect effects surrounding the decision-making processes for children in the COVID-19 pandemic reality. These decisional processes must sustain the child’s right to participation to ascertain that the action taken is in the child’s best interests. Nevertheless, the Brazilian reality has shown a structural exclusion of children’s voices in decisions affecting them, particularly concerning the effects of the pandemic on their lives. Further studies must be conducted to deepen the knowledge about children’s best interests and their participation in the actions planned during the pandemic.

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)0.000
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 score0.024
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.066
GPT teacher head0.428
Teacher spread0.363 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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