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Record W4210701044 · doi:10.47747/ijmhrr.v3i1.487

Challenges of Work-Life Balance Faced by Working Fathers in Kathmandu Valley

2022· article· en· W4210701044 on OpenAlexaff
Niranjan Devkota, Rocky Mani Shakya, Seeprata Parajuli, Udaya Raj Paudel

Bibliographic record

VenueInternational Journal of Marketing & Human Resource Research · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsWork–life balanceBalance (ability)Work (physics)Consistency (knowledge bases)PsychologyLife satisfactionInternal consistencyOrder (exchange)Social psychologyApplied psychologyBusinessComputer scienceDevelopmental psychologyEngineeringPsychometrics

Abstract

fetched live from OpenAlex

Work-life balance has been considered as important component in individual life. Today in order to attract and retain their employees organizations are considering work-life balance as their prime concern. The main purpose of this study is to examine the work-life balance of working fathers in Kathmandu Valley. This study followed descriptive method of data analysis and 405 samples were collected with the help of non-probability sampling method. Further, the study revealed that satisfaction and motivation are considered to be key factors that help to maintain work-life balance. However unhelpful attitude of colleagues is found to be major reason creating work-life unbalance. 77.03% working fathers stated that they face challenge in maintaining work-life balance. This, it is recommended that by maintaining structural consistency in the workplace challenges of work-life can be mitigated up to certain extent. These findings will have implication at organizational level. The study concluded that high level stress, unsupportive relationships, unrealistic demands, unhelpful attitude and lack of control were found to be major challenges for maintaining work-life balance.

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)0.039
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0390.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.002
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 head0.107
GPT teacher head0.394
Teacher spread0.287 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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