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Record W4318189309 · doi:10.1080/07448481.2022.2153601

COVID-19 and mental health among college students in the southwestern United States

2023· article· en· W4318189309 on OpenAlexaff
Megan Lindsay Brown, Claire E. Trotter, Wen Huang, Kaitlyn Contreras Castro, William Dylan DeMuth, Eric G. Bing

Bibliographic record

VenueJournal of American College Health · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsImpact
Fundersnot available
KeywordsWorryMental healthAnxietyCoronavirus disease 2019 (COVID-19)Depression (economics)MoodPsychologyPandemicDemographicsPatient Health QuestionnaireClinical psychologyCollege healthPsychiatryMedicineGerontologyDepressive symptomsFamily medicineDemography

Abstract

fetched live from OpenAlex

Objective: We examined COVID-19-related experiences, mental health, and future plans among US undergraduate and graduate students in the initial months of the pandemic. Participants: 72 students (68% female; 51.4% white; age x– =24.4) from 21 colleges in the US southwest concurrently enrolled in a stress-reduction study. Methods: Between March and June 2020, participants completed an online survey about demographics, personal and vicarious COVID-19 experiences, mood, and future plans. Anxiety and depression symptoms were assessed with the GAD-7 and PHQ-9, respectively. Results: Worry about COVID-19 was associated with anxiety and depression symptoms and personal and vicarious experiences with COVID-19. COVID-19 worry varied by illness severity and level of intimacy with those impacted. Most participants reported changing educational (66.7%) and life (55.6%) plans due to COVID-19. Conclusions: Given the continued impact of COVID-19 on physical/emotional health and future plans, universities should assist students in managing COVID-19-related stress so they can continue to learn and grow.

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)0.004
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 score0.155
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
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 head0.052
GPT teacher head0.445
Teacher spread0.393 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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