MétaCan
Menu
Back to cohort
Record W4285090582 · doi:10.52214/cice.v24i2.9212

Resilience and Despair

2022· article· en· W4285090582 on OpenAlexafffundabout
Frank O. Ely, Fallon R. Mitchell, Katherine E. Hirsch, Michael Diana, Krista J. Munroe‐Chandler, Paula van Wyk, Cheri L. McGowan

Bibliographic record

VenueCurrent Issues in Comparative Education · 2022
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of Windsor
FundersUniversity of Windsor
KeywordsMental healthPandemicPsychologyMedical educationPsychological resilienceDistance educationHigher educationCoronavirus disease 2019 (COVID-19)MedicinePedagogySocial psychologyPolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

The purpose of the current study was to explore graduate students’ mental health and educational experiences during the COVID-19 pandemic. Graduate students (N = 28) in Canada completed an online survey consisting of both closed- and open-ended questions related to their mental health, degree progress, and access to campus workspace. Data were analyzed using both quantitative and qualitative approaches before being synthesized through a pillar integration joint display to merge study findings. Based on self-report data, approximately 60% of participants were experiencing poor-to-moderate mental health at the time of the survey. Participants also expressed dissatisfaction with online learning and felt uncertain about their degree trajectory due to changes and restrictions associated with the pandemic. Based on the participants’ responses, recommendations for assisting graduate students during the pandemic are presented. Highlighted by these recommendations is the importance of accessing workspace on campus and the challenges associated with university mental health resources. Overall, nearly 16 months into the pandemic, participants’ mental health was negatively impacted by the restrictions. Although the study findings may not be generalizable to all post-secondary institutions, they can be used to inform university administrators regarding the continued challenges facing graduate students during 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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

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

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 head0.188
GPT teacher head0.552
Teacher spread0.364 · 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 designNot applicable
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

Citations1
Published2022
Admission routes3
Has abstractyes

Explore more

Same venueCurrent Issues in Comparative EducationSame topicCOVID-19 and Mental HealthFrench-language works237,207