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Record W4388773517 · doi:10.1177/13591053231208620

South African emerging adults’ capacity for resilience in the face of COVID-19 stressors

2023· article· en· W4388773517 on OpenAlexaff
Kate Cockcroft, Mike Greyling, Ansie Fouché, Michael Ungar, Linda Theron

Bibliographic record

VenueJournal of Health Psychology · 2023
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsDalhousie University
FundersNational Institute for the Humanities and Social Sciences
KeywordsStressorCoronavirus disease 2019 (COVID-19)Resilience (materials science)2019-20 coronavirus outbreakFace (sociological concept)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Psychological resiliencePandemicPsychologyMedicineSocial psychologySociologyClinical psychologyVirologyOutbreak

Abstract

fetched live from OpenAlex

Little is known about resilience responses to COVID-19 stressors from emerging adults in minority world contexts. In this cross-sectional study, we explored the association between self-reported COVID-19 stressors and capacity for resilience in 351 emerging adults (Mean age = 24.45, SD = 2.57; 68% female) who self-identified as Black African. We were interested in whether age, gender and neighbourhood quality influenced this association. The main findings were that higher pandemic stress was associated with a greater capacity for resilience. Older participants showed higher levels of resilience, while there was no gender difference in this regard. Those who perceived their neighbourhoods as being of a good quality also showed greater capacity for resilience, despite all participants residing in disadvantaged communities. The theoretical and practical implications of these results are considered.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.130
GPT teacher head0.503
Teacher spread0.373 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2023
Admission routes1
Has abstractyes

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