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Record W4407290863 · doi:10.1177/08861099251317493

The Cost of Caregiving: The Disproportionate and Invisible Impact of COVID-19 on Women

2025· article· en· W4407290863 on OpenAlexafffund
Jane E. Sanders, Emily Carrothers, Olivia Gamble, Derek Neeb, M.K. Arundel

Bibliographic record

VenueAffilia · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsThe King's UniversityWestern University
FundersSocial Sciences and Humanities Research Council of CanadaKing's University College
KeywordsThematic analysisCoronavirus disease 2019 (COVID-19)Mental healthPandemicPsychologyEquity (law)ReflexivityGender equityMedicineSociologyGender studiesPolitical scienceQualitative researchPsychiatrySocial science

Abstract

fetched live from OpenAlex

The COVID-19 pandemic had a global impact, altering how society and those within it function. While these changes affected almost everyone, there is evidence that women were disproportionately impacted. This reflexive thematic analysis included data gathered from parents, social work students, university faculty, and school board professionals (n = 113) from 2020 to 2023. We explored the experiences of female-identified parents and caregivers during COVID-19. The findings underscore the intense but often invisible burden that women, specifically mothers and caregivers, experienced throughout the pandemic. We found that women experienced acute stress in the realms of paid and unpaid labour, childcare, children's academics, and family mental health. Implications include a call to acknowledge this disproportionate impact and its consequences for gender equity, a recognition of the ongoing impact of COVID-19 on children, youth, families, and women, a recognition of the invisible labour that often falls upon women, and the implementation of critical methodologies in social work education and research to foster the use of such approaches within the field.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.109
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.024
GPT teacher head0.359
Teacher spread0.335 · 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 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
Published2025
Admission routes2
Has abstractyes

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