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Record W2905497915 · doi:10.5539/gjhs.v10n12p136

Absenteeism and Associated Factors in Workers of a High-Level Educational Institution, Cartagena-Colombia

2018· article· en· W2905497915 on OpenAlexvenueno aff
Regina Dominguez, Rocío García Romero, Diana Saldarriaga, Raimundo Castro-Orozco

Bibliographic record

VenueGlobal Journal of Health Science · 2018
Typearticle
Languageen
FieldPsychology
TopicStress and Burnout Research
Canadian institutionsnot available
Fundersnot available
KeywordsAbsenteeismMedicineAbdominal obesityBlood pressureDemographyEnvironmental healthObesityPopulationPhysical examinationGerontologyWaistInternal medicinePsychology

Abstract

fetched live from OpenAlex

To analyze the sociodemographic characteristics, organizational factors and cardiovascular risk factors related to work absenteeism in a higher education institution in the city of Cartagena-Colombia. Cross-sectional analytical study with a probabilistic sample of 162 workers. We recorded sociodemographic data, personal and family history, in addition, we performed a physical examination that included: abdominal circumference, height, weight and blood pressure. Also, clinical laboratory tests were performed for the analysis of lipid profile (total cholesterol, HDL cholesterol and triglycerides) and fasting blood glucose, determined by enzymatic colorimetric and automated methods. A frequency of absenteeism of 24.7% was estimated, being more frequent in the age group of 40 to 49 years and with a statistically significant difference between women and men. All the organizational variables studied showed a statistical association with work absenteeism. In contrast, the only cardiovascular risk factors that showed statistical association were: abdominal obesity and personal history of arterial hypertension. The evidences found allow us to think about the need to implement, immediately, a program of lifestyle change and healthy work, which includes a motivational strategy of change that, together, reduce the occurrence of absenteeism and the prevalence of cardiovascular risk factors found in the study population.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.008
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.070
GPT teacher head0.426
Teacher spread0.356 · 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
Published2018
Admission routes1
Has abstractyes

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