Bibliographic record
Abstract
Objectives: We evaluated the association between job stress and alexithymic traits in Korean workers. Methods: Workers (n=521) who visited two university hospitals for medical check-ups were recruited for this study. Job stress was evaluated using the Korean version of Karasek’s Job Content Questionnaire (JCQ), and alexithymic traits were assessed using the Korean version of the Toronto Alexithymia Scale (TAS-20K). Crude and adjusted odds ratios (ORs) of job stress scales (job strain, job insecurity, and job dissatisfaction) with alexithymic traits(total TAS score ≥52) were calculated. Results: High job strain compared with low strain had a high, but insignificant association with alexithymic traits (adjusted OR, 2.26; 95% CI, 0.93-5.44). High job insecurity (adjusted OR, 2.26; 95% CI, 1.21-4.22), and high job dissatisfaction (adjusted OR, 1.99; 95% CI, 1.06-3.74) had significant associations with alexithymic traits. Conclusions: This study suggests that job stress is associated with alexithymic traits in 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".