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Record W3163084209 · doi:10.3386/w28803

Experiences and Coping Strategies of College Students During the COVID-19 Pandemic

2021· preprint· en· W3163084209 on OpenAlexafffundabout
Christine Logel, Philip Oreopoulos, Uros Petronijevic

Bibliographic record

VenueNational Bureau of Economic Research · 2021
Typepreprint
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsYork UniversityUniversity of Toronto
FundersUniversity of TorontoYork University
KeywordsSocial connectednessCoronavirus disease 2019 (COVID-19)Coping (psychology)PandemicPsychology2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Higher educationMedical educationMathematics educationSocial psychologyClinical psychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

An emerging literature documents the many challenges faced by college students during the COVID-19 pandemic.Little is known, however, about how students responded to the adversity.Focusing on two large Canadian universities, we provide some of the first evidence on the coping strategies students reported and the relationships between their endorsement of specific coping strategies and their subsequent well-being.Students focused on compensating for a lack of structure by creating new routines, maintaining social connections, and trying new activities.Conditional on baseline problems indexes, students who initially endorsed social connectedness as a strategy score significantly higher on a comprehensive well-being index five to twelve weeks later.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.049
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.497
GPT teacher head0.628
Teacher spread0.131 · 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

Citations37
Published2021
Admission routes3
Has abstractyes

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