Work-family enrichment as a mediator effect of supervisor support, self-esteem, and optimism on job satisfaction
Bibliographic record
Abstract
This study aims to examine the work-family enrichment (WFE) model as influence mediator of supervisor support, self-esteem, and optimism on job satisfaction. The population in this study is 263 permanent employees of Bank BPD DIY, who occupy the positions as staff, and are married, in 7 branch offices in the Special Region of Yogyakarta (DIY). This research uses census method, while data analysis uses AMOS. The results of this study show that work-family enrichment model acts as influencer mediator of supervisor support, self-esteem, and optimism on job satisfaction fit with the empirical data. Hypothesis testing results show that supervisor support implies positive effect on job satisfaction, self-esteem implies positive effect on job satisfaction, and optimism implies positive effect on job satisfaction. Supervisor support also implies positive effect on workfamily enrichment, self-esteem implies positive effect on work-family enrichment, and optimism implies positive effect on work-family enrichment and work-family enrichment implies positive effect on job satisfaction. Work-family enrichment works significantly as influence mediator of supervisor support on job satisfaction, work-family enrichment works significantly as influence mediator of self-esteem on job satisfaction, and work-family enrichment works significantly as influence mediator of optimism on job satisfaction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".