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Estrés Laboral y Rendimiento Cognitivo de funcionarios de una Universidad en Chile

2025· article· W7116769622 on OpenAlexaboutno aff
Catalina Belén Cárdenas Lagos, Victoria Alicia Bárbara Fuentes Norambuena, Barbara Cerda Aedo, Javiera Cerda Aedo, Patricio Torres

Bibliographic record

VenueMedicina y Seguridad del Trabajo · 2025
Typearticle
Language
FieldPsychology
TopicStress and Burnout Research
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentAffect (linguistics)CognitionCognitive impairmentWork stressTest (biology)Descriptive researchScale (ratio)Stress (linguistics)

Abstract

fetched live from OpenAlex

Introduction: Nowadays, stress has become a societal problem, especially work-related stress, as excessive stress leads to a decline in workers’ cognitive abilities, disrupting their mental health and affecting their daily lives. Method: A quantitative descriptive cross-sectional study. To assess stress and cognitive impairment in the workplace among professors, the Montreal Cognitive Assessment (MoCA) and the Perceived Stress Scale (PSS-14) were used Results: The results showed that the minimum score obtained on the MoCA test was 21 points, corresponding to mild cognitive impairment (MCI), while the maximum score was 29 points. The average MoCA score was 25.55 ± 2.18 points. Regarding total stress, the minimum recorded score was 22, with no participant free of stress, and the maximum score was 39. The average total stress score was 31.95 ± 4 points Conclusions: There is an urgent need to implement strategies to prevent work-related stress factors that affect cognitive functioning. To this end, it is essential to clearly define job positions and responsibilities, provide adequate and comfortable working conditions, efficiently organize activities, offer training, provide support for personal problems, and promote work flexibility, among other measures.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.014
GPT teacher head0.370
Teacher spread0.357 · 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.

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
Published2025
Admission routes1
Has abstractyes

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