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Record W4405319950 · doi:10.1080/07448481.2024.2435955

The inequitable psychological impacts of the COVID-19 pandemic on post-secondary students with preexisting health conditions: A longitudinal study

2024· article· en· W4405319950 on OpenAlexafffund
Sarah Kuburi, Chloe A. Hamza, A. Lorenzo, Altea Kthupi, Shaza A. Fadel, France Gagnon

Bibliographic record

VenueJournal of American College Health · 2024
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersUniversity of Toronto
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Psychological distressMental healthLongitudinal studyYoung adult2019-20 coronavirus outbreakPsychologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicineDistressDemographyClinical psychologyPsychiatryDevelopmental psychologyVirologyInfectious disease (medical specialty)DiseaseOutbreakSociology

Abstract

fetched live from OpenAlex

Objective: Evidence suggests young adults in post-secondary school experienced increased distress during the COVID-19 pandemic, but students’ experiences likely varied. Effects may have also changed over time as students adapted. This study examined the mental health of students with and without preexisting health conditions at two points during the pandemic (winter 2020/2021 and spring/summer 2021). Methods: Participants (N = 1465) completed a baseline and follow-up questionnaire assessing their health history, depressive symptoms, anxiety symptoms, stress, and COVID-19-related worry and perceived vulnerability of severe infection. Results: At both time points, students with preexisting health conditions reported greater distress than those without preexisting health conditions. Stress increased from time 1 to time 2 for all students, and participants with preexisting health conditions significantly increased in their COVID-19 perceived vulnerability of severe infection over time. Conclusions: Findings highlight the need for additional and ongoing mental health supports for vulnerable students throughout the pandemic.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.039
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.126
GPT teacher head0.513
Teacher spread0.387 · 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.

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

Citations0
Published2024
Admission routes2
Has abstractyes

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