The Effects of Job Quality on the Health of Wage Workers: Congruence between the Hard and Soft Job Quality
Bibliographic record
Abstract
Background: This study analyzes the linear and non-linear effects of the hard and soft dimensions of job quality on the overall health of wage workers. It also examines the congruence or fit between the hard and soft job quality on the overall health of wage workers. Methods: This study measured thirty indicators that constitute job quality and reduced the indicators into twelve sub-dimensions of job quality using reflective factor analysis. In addition, this study derived two dimensions of job quality from the twelve subdimensions, namely the hard and soft job quality using formative factor analysis. This paper applied the response surface analysis to analyze the congruence effect between the two dimensions of job quality. Results: A logarithmic relationship was found between the dimension of hard job quality and the worker's overall health. This study also verified that the congruence effect between the two dimensions of job quality does not exist, and the combined effect of job quality is lower when the two dimensions of job quality are at the same level than the effect when either level of job quality is high or low. Conclusions: Although hard and soft job quality has independent positive effects on the overall health of wage workers, the two dimensions of job quality are not congruent or not in harmony with each other. This incongruence between hard and soft job quality, together with a higher impact of hard job quality, suggests that the role of soft job quality on overall health is relatively limited.
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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 | 0.003 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".