Relationship between mental fatigue and cognitive functions in energized line workers of electric sector companies in Aragua State-Venezuela, 2024
Bibliographic record
Abstract
Introduction: Understanding the physical and mental state of operational personnel in the electrical sector was essential to ensure health and prevent workplace accidents. This research analyzed the relationship between mental fatigue and cognitive functions in energized line workers of electric companies in Aragua state, Venezuela. Methods: The study was developed under the positivist paradigm with a quantitative approach, non-experimental, descriptive, field-based, and cross-sectional design. The sample consisted of twelve workers assigned to energized line departments. Data collection was conducted through observation, photographic records, sociodemographic survey, Montreal Cognitive Assessment Test version 8.1 (MoCA), and Subjective Fatigue Patterns Test (PSF). Pearson's correlation coefficient was used to analyze the relationship between variables. Results: The results showed a low inverse correlation, suggesting that increased fatigue levels could be associated with a decrease in cognitive function values. Conclusions: A moderately low and non-significant statistical correlation was concluded, suggesting the need for future research with larger sample sizes and validation of other variables such as age, educational level, experience, as well as specific analysis of the Montreal Cognitive Test variables.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".