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Record W4294243386 · doi:10.23889/ijpds.v7i3.1839

An investigation of kindergarten educator reported barriers and concerns and school neighbourhood composition in Ontario, Canada.

2022· article· en· W4294243386 on OpenAlexaffabout
Natalie Spadafora, Caroline Reid‐Westoby, Molly Pottruff, Jade Wang, Eric Duku, Magdalena Janus

Bibliographic record

VenueInternational Journal for Population Data Science · 2022
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNeighbourhood (mathematics)Poisson regressionPsychologyCoronavirus disease 2019 (COVID-19)CensusSocioeconomic statusMedical educationMedicineEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

ObjectivesThe COVID-19 pandemic has not impacted everyone equitably, including children (e.g., Li et al., 2021). The objective of this study was to explore the association between school neighbourhood composition and kindergarten educator-reported barriers and concerns regarding children’s learning during the first wave of COVID-19 related school closures in Ontario, Canada. ApproachIn the spring of 2020, we collected data from Ontario kindergarten educators in an online survey on their experiences and challenges with online learning during the first round of school closures. We asked educators whether they experienced a number of barriers to learning and concerns about returning to school in the Fall. We linked the educator responses to 2016 Canadian Census variables based on the school postal code. Poisson regression analyses were used to determine if there was an association between neighbourhood composition and the number of barriers and concerns reported by kindergarten educators. ResultsEducators (n = 2569; 74.2% kindergarten teachers, 25.8% early childhood educators; 97.6% female) who taught at schools in neighbourhoods with lower median income reported a greater number of barriers to online learning (e.g., students' lack of access to electronic devices) and concerns regarding the return to school in the fall of 2020 (e.g., concerned about differences in how much students learned during the school closures). Educators also reported a greater number of concerns regarding the return to the classroom in neighbourhoods with a greater proportion of single-parent families. ConclusionOur study confirms that the educational impacts of the COVID-19 pandemic may not have been felt equitably even by kindergarten children, as educators teaching in schools in lower SES neighbourhoods reported both more barriers to online learning, and more concerns about returning to the classroom in September 2020.

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)0.002
metaresearch head score (Gemma)0.005
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.402

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0080.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0000.001
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 head0.091
GPT teacher head0.431
Teacher spread0.340 · 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
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

Citations1
Published2022
Admission routes2
Has abstractyes

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