Areas of worklife as predictors of occupational health – A validation study in two German samples
Bibliographic record
Abstract
Background/aim Occupational health largely depends on the perceived fit between the employee's abilities and workplace demands/factors. The Areas of Worklife Scale (AWS) specifies six areas that are particularly relevant in this respect: workload, control, reward, community, fairness, and values. The current article aimed at investigating the factorial structure and the criterion validity of the German translation of the AWS. Methods Data were collected in two samples. In study 1, 1455 public service workers were surveyed using the six areas of worklife and well-being. In study 2, to investigate the well-established relationship between the AWS and burnout, the scale was administered to a nursing sample ( N = 443). Results High internal consistencies for all six scales were obtained in both studies. Exploratory as well as confirmatory factor analysis replicated the theoretically assumed six scale structure of the AWS. Evidence of criterion validity was found by multiple linear regression analysis with well-being as dependent measure (study 1). SEM analyses supported the hypothesized relationships between the six AWS dimensions and burnout (study 2). As predicted by Leiter and Maslach, 2004 , Leiter and Maslach, 2009 , only some areas were directly associated with the health-related outcomes (well-being and burnout). In line with previous work, workload and values proved to be the most critical areas of worklife. Conclusions The six areas of worklife have been shown to be significant predictors of health-related outcomes. Based on the current studies, the German translation of the AWS can be proposed as a reliable and valid instrument to identify and specify critical work-related areas for occupational health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".