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Record W4301240797 · doi:10.25071/1929-8471.95

Academic mothers during the COVID-19 pandemic: Stressors, strains, and challenges in adapting to work-life enmeshment

2022· article· en· W4301240797 on OpenAlexaff
Chang Su, Tsorng-Yeh Lee, Gordon L. Flett

Bibliographic record

VenueINYI Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsResearch CanadaOntario Centre of Excellence for Child and Youth Mental HealthYork UniversityBrandon University
Fundersnot available
KeywordsLonelinessPandemicAdaptabilityDisconnectionWork (physics)PsychologyPsychological resilienceWorkloadPersonal lifeMental healthStressorCoronavirus disease 2019 (COVID-19)SociologyPublic relationsPolitical scienceSocial psychologyMedicineClinical psychologyManagementEngineeringPsychiatry

Abstract

fetched live from OpenAlex

The COVID-19 pandemic had numerous unexpected impacts on academic mothers around the world. In the current article, the challenges being faced by academic mothers during the pandemic are illustrated based on recently published peer-reviewed and grey articles. The enmeshment of work and family life and the lack of separation from work increases the possibilities of significant professional challenges and possible mental health and physical health problems. Specific themes are highlighted, including strains of learning new technologies for online teaching, increasing workload, and household chores, barriers to scholarly productivity, insufficient support from institutions, loneliness due to disconnection, and pursuing perfection. The need for adaptability is also highlighted. This article also provides some institutional recommendations designed to support various academic mothers in increasing their empowerment, adaptability, and resilience, when they are facing the enmeshment of work and life. Given that the pandemic is continuing and now clearly represents a prolonged stress sequence, it is essential that academic mothers develop and utilize positive resources in order to limit the impact on their personal and professional lives.

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)0.004
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.153
GPT teacher head0.354
Teacher spread0.201 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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