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Record W2490117670 · doi:10.5539/mas.v10n12p82

Checking the Relationship between Quality of Work Life and Employees Job Satisfaction of Education District 10 of Tehran

2016· article· en· W2490117670 on OpenAlexvenueno aff
Rogheyeh Najafifar

Bibliographic record

VenueModern Applied Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Spirituality and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsJob satisfactionDescriptive statisticsPsychologyQuality (philosophy)Descriptive researchTest (biology)Quality of working lifeProductivityVariablesWork (physics)Sample (material)Job performanceStatistical populationApplied psychologySample size determinationPopulationSocial psychologyStatisticsMathematicsDemographySociologyEngineering

Abstract

fetched live from OpenAlex

Considering the fact that in all of the organization including education organization quality of work life today has found a special place, because this reason lack and vacuum of working life quality cause employees have not spirit that they should have to work, and this leads to job satisfaction and ultimately employee productivity reduce and working environment and career seem hollow. So in this research tried to assess the relationship between work life quality and job satisfaction in Education Organization of Tehran. This research was conducted in period is July and August 2015. The research method is descriptive and correlational and the study population included all employees in the Education Organization 10 District of Tehran. The total number is 100 people. That according to Cochran formula was calculated sample size of over 79 people. In this study, for the data analysis, descriptive and inferential statistical methods such as Kolmogorov - Smirnov correlation test was used. The study results showed that 8 independent variable of this research significantly have significant relationship with dependent variable job satisfaction.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.068
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.142
GPT teacher head0.370
Teacher spread0.228 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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