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Record W4404433093 · doi:10.1002/acp.70008

Rapid Learning in Frontline Grocery Workers During the <scp>COVID</scp>‐19 Pandemic

2024· article· en· W4404433093 on OpenAlexafffundabout
Julia G. Halilova, Deltcho Valtchanov, R. Shayna Rosenbaum

Bibliographic record

VenueApplied Cognitive Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsBaycrest HospitalYork University
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence Fund
KeywordsCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PsychologyGrocery storeVirologyAdvertisingBusinessMedicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

ABSTRACT Prolonged stress and the need for rapid uptake of information can have detrimental effects on memory and cognition, whereas meaningfulness of study material and motivation to learn can have positive effects. How do these opposing conditions impact workplace learning in essential frontline workers during a global pandemic? We analyzed learning data collected longitudinally since before the pandemic in over 85,000 essential frontline grocery workers and nonessential telecommunications workers via a learning management system that incorporates a spaced retrieval schedule, where items are retrieved following retention intervals of varying length. Findings indicate more rapid knowledge uptake in grocery workers (a) during than before the pandemic, (b) for COVID‐19‐related content than non‐COVID content, and (c) in the United States than in Canada. Longer‐term maintenance of training material was similar across groups. Evidence of enhanced workplace learning and retention supports efforts to integrate empirically based strategies from the behavioral sciences into learning‐based technologies.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.002

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 head0.067
GPT teacher head0.407
Teacher spread0.341 · 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.

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

Citations3
Published2024
Admission routes3
Has abstractyes

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