A factor confirmation and convergent validity of the “areas of worklife scale” (AWS) to Spanish translation
Bibliographic record
Abstract
BACKGROUND: Perceived incongruity between the individual and the job on work-life areas such as workload, control, reward, fairness, community and values have implications for the dimensions of burnout syndrome. The "Areas of Work-life Scale" (AWS) is a practical instrument to measure employees´ perceptions of their work environments. AIMS: Validate a Spanish translation of the AWS, and it relationship with Masclach Burnout Inventory dimensions. METHODS: The study was conducted in three medium-sized hospitals and seven rural and urban Primary Care centres (N = 871) in Spain. The "Maslach Burnout Inventory General Survey" (MBI-GS) and AWS were applied. We developed a complete psychometric analysis of its reliability, and validity. RESULTS: Data on the reliability supported a good internal consistency (Cronbach α between .71, and .85). Construct validity was confirmed by a six factor model of the AWS as a good measure of work environments (χ²(₃₅₂) = 806.21, p < 0.001; χ²/df = 2.29; CFI = 0.935, RMSEA = 0.039); concurrent validity was analysed for its relationship with other measures (opposing dimensions to burnout, and MBI), and each correlation between dimensions and sub-dimensions were statistically significant; as well, predictive validity, by a series of Multiple Regression Analysis examined the resulting patterns of the Confirmatory Factor Analysis (CFA) confirms the relationship between the work-life areas and burnout dimensions. CONCLUSIONS: Leiter and Maslach's AWS has been an important instrument in exploring several work-life factors that contribute to burnout. This scale can now be used to assess the quality of work-life in order to design and assess the need for intervention programs in Spanish-speaking countries.
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 | 0.011 | 0.027 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".