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Record W4406655777 · doi:10.56294/hl2024.438

Relationship between mental fatigue and cognitive functions in energized line workers of electric sector companies in Aragua State-Venezuela, 2024

2024· article· en· W4406655777 on OpenAlexaboutno aff
M. Melendez, Evelín Escalona, Misael Ron

Bibliographic record

VenueHealth Leadership and Quality of Life · 2024
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsnot available
Fundersnot available
KeywordsState (computer science)Line (geometry)BusinessMathematics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.512
GPT teacher head0.532
Teacher spread0.019 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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