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Record W4405368770 · doi:10.18357/ijcyfs154202422216

FAMILY CHALLENGES AND COPING MECHANISMS DURING THE COVID-19 PANDEMIC: WESTERN CAPE, SOUTH AFRICA

2024· article· en· W4405368770 on OpenAlexvenueno aff
Zainab Kader, Fatiema Benjamin, Donnay Manuel, Mulalo Mpilo, Simone Titus, Nicolette V. Roman

Bibliographic record

VenueInternational Journal of Child Youth and Family Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsnot available
FundersUniversity of the Western CapeNational Research Foundation
KeywordsSnowball samplingPandemicCoping (psychology)PovertyThematic analysisNonprobability samplingSocioeconomicsExploratory researchCoronavirus disease 2019 (COVID-19)Qualitative researchEconomic growthGeographyPsychologySociologyEnvironmental healthMedicineSocial scienceDiseasePopulationClinical psychology

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has impacted the health and well-being of families in South Africa, amplifying family challenges and requiring modifications to their coping mechanisms. While the pandemic has been successfully managed in South Africa, some challenges, such as those related to poverty, loss of income, and economic uncertainty, have been exacerbated. This study, which used an exploratory qualitative research design, sought to offer insight into the coping mechanisms of South African families used to deal with family challenges during the pandemic. Through purposive and snowball sampling, 31 participants were recruited; the majority were living in a nuclear family, but some had other arrangements. The participants were from six municipal districts in the Western Cape Province. Semi-structured interviews were conducted, and data were analysed using thematic analysis. The findings of this study demonstrate that the coping mechanisms families used during the COVID-19 pandemic were largely drawn from internal resources.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.109
GPT teacher head0.341
Teacher spread0.232 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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