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Social and Psychological Support for Personnel in Organisations: Work-Life Balance Programmes

2020· article· en· W3091268077 on OpenAlexvenueno aff
Marianna Tkalych, Iryna Snyadanko, N. O. Guba, Yuliia Zhelezniakova

Bibliographic record

VenueJournal of Intellectual Disability - Diagnosis and Treatment · 2020
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsnot available
Fundersnot available
KeywordsWork–life balanceBalance (ability)Work (physics)PsychologySocial supportPublic relationsApplied psychologyBusinessSocial psychologyPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Objective: The study aimed to explore the work-life balance concept, assessing the level and the peculiarities of work-life balance. Background: The paper covers the psychological reconstruction of the concept of "the work-life balance" in terms of its implicit understanding and differentiated diagnosis. Method: The main research methods are: theoretical – the study of literature on the research problem; empirical – a study on the methodology for assessing psychological well-being, as well as methods of descriptive and analytical statistics. Results: It has been designed an inventory based on 16 statements divided into the work-life balance components by means of the factor analysis. Satisfaction with work and family roles have been found to have additive effects on happiness, life satisfaction, and perceived quality of life. The basic strategies in social and psychological programs in organisations have been analysed as focus strategies, "resource/refusal" strategies, adaptive strategies. Adaptive strategies are the ones helping to adapt to problems arising at work or/and at home. Conclusion: Empirical findings suggest that involvement in multiple roles can improve psychological and mental health by buffering negative effects such as reduced. The use of individual adaptive strategies with the resource increase strategy perfectly contributes to the work-life balance achievement.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.032
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.165
GPT teacher head0.441
Teacher spread0.276 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Intellectual Disability - Diagnosis and TreatmentSame topicWorkplace Health and Well-beingFrench-language works237,207