The Importance of Cognitive and Mental Factors on Prediction of Job Performance in Chinese High-Speed Railway Dispatchers
Bibliographic record
Abstract
High-speed railway (HSR) transportation poses a serious challenge to dispatchers, whose job performance plays a critical role for the safety and efficiency of the transportation system. This study examined the relationship between cognitive and mental factors and job performance among Chinese high-speed railway dispatchers and established a predictive model for the job performance of dispatchers. Cognitive abilities involved in train dispatching including working memory and multiobject tracking and potential related mental factors including depression, anxiety, perceived stress, and social support were examined. Job performances were measured by both subjective and objective indicators, i.e., the overall evaluation by supervisors and the delay time by dispatching simulator. Stepwise regression results showed that both cognitive abilities and 2 mental factors (depression and perceived stress) have strong relations with job performance, and tremendous distinction between groups of good and poor performance of HSR dispatchers is revealed. The predictive model accounted for 91% of the overall variance in objective performance indicator and has 96% distinguished accuracy of good and poor groups. These findings imply that cognitive and mental factors should be of great concern to the current practice of Chinese HSR dispatcher selection and management.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".