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Record W4226195146 · doi:10.1177/21582440221082148

The Resilience and Mental Health Experiences of Emerging Adults During the COVID-19 Pandemic: Creating Safeguards for the Future

2022· article· en· W4226195146 on OpenAlexaffabout
Jillian Roberts, Bianca Humbert, Robyn MacMillan, Celeste Duff

Bibliographic record

VenueSAGE Open · 2022
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMental healthPandemicPsychological resiliencePsychologyCoronavirus disease 2019 (COVID-19)Diversity (politics)Public healthNarrativePublic relationsMedical educationSociologyPolitical scienceMedicineSocial psychologyNursingPsychiatry

Abstract

fetched live from OpenAlex

There is limited research on the mental health impacts of the COVID-19 pandemic on emerging adults from diverse communities, including those with disabilities, international students, and students who identify as part of the LGBTQ2AAI+ community. A purposeful sample of seven undergraduate students, between the ages of 19 and 30, at a university in British Columbia, Canada, participated in this study. In-depth narrative style interviews were conducted via Zoom. Data were analyzed thematically and from a resilience lens framework. This study demonstrates that participants experienced a diversity of challenges, and thus engaged in differing processes of adjustment. Four protective factors were identified: (1) Positive relationships; (2) Perceived efficacy; (3) Purpose and ambition; and (4) Sense of normality. This study contributes towards the limited research base, and thus offers valuable insights, which can inform university policy makers, university administration, and public health policy makers to be better positioned to develop innovative adaptions of services and/or delivery.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.030
GPT teacher head0.428
Teacher spread0.398 · 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.

Study designQualitative
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

Citations5
Published2022
Admission routes2
Has abstractyes

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