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Record W4200523388 · doi:10.3390/educsci11120796

Trauma-Informed School Strategies for SEL and ACE Concerns during COVID-19

2021· article· en· W4200523388 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEducation Sciences · 2021
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversity of AlbertaUniversity of Windsor
Fundersnot available
KeywordsDisadvantagedCoronavirus disease 2019 (COVID-19)PandemicPerspective (graphical)Psychology2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicinePolitical science

Abstract

fetched live from OpenAlex

The precarious circumstances associated with the COVID-19 pandemic have raised important questions concerning the potential impact on child and adolescent development. For instance, how might this disruption influence social and emotional learning (SEL) and affect adverse childhood experiences (ACEs)? Moreover, what protective practices may be put in place to mitigate risks? The purpose of this critical review is to engage with these questions. Relevant research findings published before and during pandemic contexts are presented. Connections between SEL, ACEs and past social disruptions are substantiated in the literature. Additionally, preliminary evidence has elucidated variables associated with ACEs and SEL concerns during the pandemic. For instance, research suggests that students from socially disadvantaged positions may be disproportionately impacted by these issues. Actionable trauma-informed recommendations for educators are discussed, including creating safe school environments and adopting a strength-based perspective.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.106
GPT teacher head0.448
Teacher spread0.342 · 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