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Record W3088408241 · doi:10.3138/jcfs.51.3-4.002

Wrestling with Role Strain in a Pandemic: Family, ‘Stay-at-Home’ Directive, and the COVID-19 Pandemic

2020· article· en· W3088408241 on OpenAlexvenueno aff
Stella R. Quah

Bibliographic record

VenueJournal of Comparative Family Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicPublic healthCoping (psychology)DirectiveContext (archaeology)Qualitative researchCoronavirus disease 2019 (COVID-19)PsychologyMedicineSociologyNursingGeographyPsychiatrySocial scienceInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

This is an exploratory proof of concept analysis of individuals and families experiencing the public health crisis of the COVID-19 pandemic in 16 countries around the globe. The objective is to explore how individuals and families are coping with their regular and new roles during the COVID-19 pandemic in general and, in particular, under the “Stay-at-Home Directive” (SAHD). SAHD is one of the crucial public health directives to contain the pandemic. The study examines the concepts role strain, role compartmentalization and two of the three types of health-related behaviour (preventive health behaviour and sick-role behaviour). The secondary data examined through qualitative content analysis are 75 personal interviews with individuals published in the news media. The interviews were identified using a systematic search of published news media articles around the world in FACTIVA during the period 1 January to 30 March 2020, the first three months of the COVID-19 pandemic. The findings confirm the positive influence of role compartmentalization in decreasing role strain in the context of a public health crisis as families re-arrange their lives under SAHD. This influence appears stronger among healthy families (not yet infected by the virus) and among individuals who are parents.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.821

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.205
GPT teacher head0.402
Teacher spread0.197 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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