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Record W2601366409 · doi:10.3968/9224

Emotional Burnout Among Working Wives: Dimensions and Effect

2017· article· en· W2601366409 on OpenAlexvenueno aff
Rula Odeh Al Sawalqa

Bibliographic record

VenueCanadian social science · 2017
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsBurnoutEmotional exhaustionPsychologyStressorMental healthScale (ratio)Emotional laborSocial psychologyClinical psychologyDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

Emotional burnout of the most important psycho-social phenomena which has roots Grounded in social relations—reactions in social interactions—especially in the work environment, and it goes beyond mental health by focusing on specific stressors in the workplace to emphasize total life and environmental pressures affecting health. This article examines and reveals the levels of Emotional Burnout among working wives, and how this relates to the demographic dimension. For this purpose, the general social surveying method was used to collect data. The Emotional Burnout scale was used to measure these levels in 600 employed women. Findings indicated that the level of Emotional Burnout among working women was moderate; and that the number of children was among the variables that had the greatest effect on raising the level of Emotional Burnout among the participants. Emotional Burnout is also harmful health-wise (headaches, eating disorders, irregularity in heart rate, stomach pain).

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)0.001
metaresearch head score (Gemma)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.037
GPT teacher head0.344
Teacher spread0.307 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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