Analysis of Factors Affecting Quality of Life of Workers in Korea Participating in Leisure Activities Using Quantile Regression
Bibliographic record
Abstract
INTRODUCTION: This study aimed to identify factors affecting the quality of life (QoL) of workers in Korea participating in leisure activities.METHODS: Cross-sectional survey data were collected from June 10 to June 20, 2013, examining QoL, job stress, social support, serious leisure, and health-related characteristics. Data from 101 participants were analyzed using t-tests, Pearson's correlation, multiple linear regression, and quantile regression.RESULTS: The workers’ mean QoL score was 23.10. Significant predictors of mean QoL score were job stress, social support, and serious leisure. Job stress correlated strongly with QoL in workers who were at 10% (QoL=17.00, p=.013) and 25% (QoL=20.00, p=.001) of the QoL distribution. Social support and serious leisure correlated significantly with QoL in workers who were at 50% (QoL=24.00) and 75% (QoL=27.00) of the QoL distribution.CONCLUSION: Quantile regression analysis identified factors affecting QoL in workers. Therefore, intervention strategies for increasing workers’ QoL should be tailored to workers’ QoL level.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".