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Record W2071683958 · doi:10.1111/joop.12071

Work–family interference, psychological distress, and workplace injuries

2014· article· en· W2071683958 on OpenAlexafffund
Nick Turner, M. Sandy Hershcovis, Tara C. Reich, Peter Totterdell

Bibliographic record

VenueJournal of Occupational and Organizational Psychology · 2014
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsConservation of resources theoryPsychologyPsychological distressDistressWork (physics)Work–family conflictSample (material)Social psychologyOccupational safety and healthSAFERApplied psychologyClinical psychologyMental healthPsychiatryMedicineComputer security

Abstract

fetched live from OpenAlex

We draw on conservation of resources theory (Hobfoll, ) to investigate in two studies the relationship between work–family interference (i.e., work–family conflict and family–work conflict) and workplace injuries as mediated by psychological distress. In S tudy 1, we use split survey data from a sample of UK health care workers ( N = 645) to first establish the model, and then cross‐validate it, finding that work–family conflict (but not family–work conflict) was partially related to workplace injuries via psychological distress. In S tudy 2, we extend the model with a separate two‐wave sample of manufacturing and service employees ( S tudy 2; N = 128). We found that psychological distress fully mediated the relationship between work–family conflict and workplace injuries incurred 6 months later, controlling for prior levels of workplace injuries. The implications of making workplaces safer by enabling employees to better manage competing work and home demands are discussed. Practitioner points This research illustrates how the stress from managing work and family is related to more frequent workplace injuries. Reducing psychological distress – particularly from the conflict between balancing work and home domains – may be a way of keeping workers physically safe.

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 score0.033
Threshold uncertainty score0.642

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.420
Teacher spread0.380 · 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

Citations51
Published2014
Admission routes2
Has abstractyes

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