Validation of two complementary instruments for measuring work stress in Chilean workers.
Bibliographic record
Abstract
Introduction: Instruments based on Karasek’s Demand/Control/Support and Siegrist’s Effort-Reward Imbalance models have been extensively used in the worlds to evaluate exposure to psychosocial workplace risks linked to physical and mental health, but have not been validated in Chile. Objective: To analyze the factorial structure and the concurrent and criteria validity in two complementary scales to measure psychosocial risk at work in Chile. Method: the design is transversal, the study was conducted at the national level(Chile), the random sample included 3010 workers(51% males and 49% females) in a households survey. The analysis included correlations, structural equations(SEM) and logistic regression. Results: the internal consistency of Karasek’s global scale was ? =0.74, while in Siegrist’s global scale it was ? =0.72. The models evidenced good structural adjustment (Karasek: RMSEA=0.051 and CFI=0.97; Siegrist: RMSEA=0.054 and CFI=0.98), and evidenced dose-response association between incremental exposure to the psychosocial dimensions of work and distress. Conclusion: the original theoretical constructs of the Karasek and Siegrist models are well-represented in the Chilean population. However, we suggest eliminate two social support items and adding items to the Psychological Demands scale in order to improve the consistency of Karasek’s instrument. The results support the use of instruments for research with Chilean workers.
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".