Japanese version of the Areas of Worklife Survey (AWS): Six mismatches between person and job environment
Bibliographic record
Abstract
developed a new measure, Areas of Worklife Survey (AWS) that measures six areas in the work environment.The objective of the present study is to translate the AWS into Japanese and evaluate factorial validity and examine criterion-related validity as well as reliability.The Japanese AWS was prepared and administered to a sample of employees at one IT enterprise.A total of 1,214 valid data was obtained.The AWS consists of 29 items that produce distinct scores for each of the six areas of worklife: workload, control, reward, community, fairness, and values.The exploratory factor analysis replicated the same six-factor structure as the original.The confirmatory factor analysis supported a six-factor model.Cronbach's alpha coefficients for all six subscales were .66-.88.The AWS had significant correlations with three subscales of the MBI-GS but not for workload-professional efficacy.In all, the examination found support for the validity as well as reliability of the Japanese AWS although a couple of issues to be resolved in the near future remain.Keywords： The Areas of Worklife Survey (AWS), Japanese version, reliability, validity, burnout, job stress 本研究は，平成 23 年度-25 年度科学研究費補助金 (基盤研究，課題番号 23593416，研究代表者：北岡 和代)の支援を受けた。また，本研究は金沢医科大 学疫学研究倫理審査委員会から承認を得て実施した。
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".