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Occupational Stress and Its Impact on Working Mothers During the COVID-19 Pandemic

2022· book-chapter· en· W4281807505 on OpenAlexaff
Samar Saeed Khan

Bibliographic record

VenueAdvances in human resources management and organizational development book series · 2022
Typebook-chapter
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsYork University
Fundersnot available
KeywordsNeglectDilemmaWork (physics)Work–life balanceBalance (ability)PsychologyPsychological resiliencePandemicWorkforceCoronavirus disease 2019 (COVID-19)Social psychologyPolitical scienceMedicineEngineeringPsychiatry

Abstract

fetched live from OpenAlex

Through discourse analysis methodology of an online forum and a review of the existing literature, this chapter aims to examine the interceding role of work and the moderating role of resilience in the relationship between occupational stress and well-being of working mothers during the COVID-19 pandemic. While work and family are pertinent in a woman's life, she is faced with a dilemma: to either be a full-time worker or a full-time mother; yet, she tries to balance the two, which in turn subjects her to stress, both at work and inside the domestic sphere. Not only do mothers have to manage the home, but they also have to manage/maintain their jobs. Yet, adhering to this hope of the work/life balance affects a working mother's ability to maintain paid employment or gain a promotion because she has to, at times, prioritize taking care of her children in the face of high work stress. At the same time, high work stress may be problematic, resulting in neglect. (Working mothers may be forced to dedicate time to work for fear of getting fired and ignore the needs of their children.)

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.291
Teacher spread0.266 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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