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Record W2999253196 · doi:10.1177/0734371x19894035

The Effects of Family Responsibilities Discrimination on Public Employees’ Satisfaction and Turnover Intentions: Can Flexible Work Arrangements Help?

2020· article· en· W2999253196 on OpenAlexaffabout
Lauren Bock Mullins, Étienne Charbonneau, Norma M. Riccucci

Bibliographic record

VenueReview of Public Personnel Administration · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsÉcole Nationale d'Administration Publique
Fundersnot available
KeywordsWork (physics)Job satisfactionPublic servicePublic service motivationPublic sectorTurnoverPublic relationsBusinessMatching (statistics)Service (business)PsychologySocial psychologyDemographic economicsMarketingPolitical scienceEconomicsManagement

Abstract

fetched live from OpenAlex

Discrimination against caregivers is a topic of interest for the public sector workplace. This study assesses the degree to which family responsibilities discrimination (FRD) can diminish work satisfaction and lead to intentions of leaving the public service. It also examines the effects of flexible work arrangements on work satisfaction and intentions to leave. Applying Mahalanobis Distance Matching, we examine the Canadian Public Service Employee Survey and find evidence that family status discrimination has some impact on employees’ intention to leave the public service and can diminish satisfaction with work arrangements. We also find that federal public servants who feel like work and family/personal obligations adversely affected their career progression were less satisfied with their work arrangements and were more likely to want to leave their positions.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.080
GPT teacher head0.335
Teacher spread0.255 · 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 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

Citations37
Published2020
Admission routes2
Has abstractyes

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