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Record W2097221964 · doi:10.5267/j.msl.2012.06.010

A social work study on measuring the impact of gender and marital status on stress: A case study of hydro-power employees

2012· article· en· W2097221964 on OpenAlexvenueno aff
Akbar Iravani, Mohammad Reza Iravani, Gholamali Iravani, Mahdi Khorvash, Seyed Esmael Mosavi

Bibliographic record

VenueManagement Science Letters · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Marital statusPower (physics)Work stressPsychologyStress (linguistics)Social psychologyDemographic economicsApplied psychologySociologyDemographyEconomicsEngineering

Abstract

fetched live from OpenAlex

The study performs an empirical survey to measure the impact of stress among people with various gender and marital status in a hydropower unit located in city of Esfahan, Iran. The study performs the survey among all 81 people who were working for customer service section of this company and consists of two parts, in the first part; we gather all private information such as age, gender, education, job experience, etc. through seven important questions. In the second part of the survey, there were 66 questions, which included all the relevant factors impacting employees' stress. We implement two Levin and t-student tests to see whether gender or marital status has any meaningful influences on creating stress among people. The results indicate that gender has no meaningful impact on creating stress among employees who worked for this hydro plant except difficulty of job conditions. The other findings of this paper is that stress posed from management team had different impacts on employees with various marital status but there were no meaningful differences between married and single couples in terms of other factors posing stress such as unsuitable working conditions, fear of job stability or difficulty of job conditions.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.120
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.062
GPT teacher head0.340
Teacher spread0.279 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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