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Record W3214212362 · doi:10.1139/facets-2021-0081

School recess and pandemic recovery efforts: ensuring a climate that supports positive social connection and meaningful play

2021· article· en· W3214212362 on OpenAlexaffvenueabout
Lauren McNamara

Bibliographic record

VenueFACETS · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsToronto Metropolitan UniversityRoyal Society of Canada
Fundersnot available
KeywordsHarmMental healthPandemicPsychologyPublic relationsCoronavirus disease 2019 (COVID-19)Political scienceSocial psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

As Canada’s schools reopen, attention to healing the school community is essential. Given the considerable stressors of the COVID-19 pandemic, it is unsurprising that recent studies find Canadian children’s mental health in decline. As social connection is tightly entwined with children’s mental health, supporting school-based spaces for quality social interactions and play will be an important postpandemic recovery strategy. Children will need opportunities to re-establish positive social connections at school, and informal spaces such as recess and lunch are an ideal time to afford these opportunities. Yet many schoolyards have long been challenged by social conflict that can interfere with children’s need to connect with peers. Therefore, efforts should be directed not only at mitigating the effects of social harm, but also toward ensuring social and physical landscapes that are meaningful, inclusive, and engaging for children and adolescents of all ages. Recommendations for postpandemic recovery are provided.

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)0.008
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.583
Threshold uncertainty score0.839

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0270.009
Scholarly communication0.0110.004
Open science0.0030.015
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0170.002

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 head0.048
GPT teacher head0.372
Teacher spread0.323 · 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 designQualitative
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

Citations23
Published2021
Admission routes3
Has abstractyes

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