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Record W4399265764 · doi:10.1177/2752535x241257561

Public Health Challenges for Post-secondary Students During COVID-19: A Scoping Review

2024· review· en· W4399265764 on OpenAlexaff
Pooja Dey, Leanne R. De Souza

Bibliographic record

VenueCommunity Health Equity Research & Policy · 2024
Typereview
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsInstitute for Work & HealthPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsPublic healthSocioeconomic statusMental healthPandemicRelocationCoronavirus disease 2019 (COVID-19)Social determinants of healthPsychologyHealth equityEnvironmental healthPolitical scienceEconomic growthMedicineMedical educationNursingPsychiatryEconomicsPopulation

Abstract

fetched live from OpenAlex

Research about public health impacts of COVID-19 on post-secondary students is slowly beginning to emerge. This scoping review identified common public health challenges among post-secondary students in higher-income countries during the COVID-19 pandemic. Five databases were searched to find relevant peer-reviewed literature up to March 2022. Results were categorized according to reported public health challenges and relevant socio-economic variables. After screening, 53 articles were reviewed. Most articles were from the USA (39/53). The seven main public health challenges identified were mental health (35/53), financial instability (25/53), physical health (13/53), food insecurity (12/53), social well-being (8/53), digital access (7/53), and housing or relocation (6/53). Students with low socioeconomic status experienced heightened public health challenges. This review offers insight and opportunities for the development of longitudinal tools to support social determinants of health in post-secondary populations in high-income countries and may offer insight into similar experiences for students in other settings.

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)0.068
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Open science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.688
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0680.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0040.003
Science and technology studies0.0070.001
Scholarly communication0.0000.000
Open science0.0060.009
Research integrity0.0010.012
Insufficient payload (model declined to judge)0.0010.001

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 head0.871
GPT teacher head0.742
Teacher spread0.128 · 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; both teacher heads agree on what is shown here.

Study designSystematic review
Domainnot available
GenreReview

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

Citations6
Published2024
Admission routes1
Has abstractyes

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