Factores de riesgos laborales que afectan a los conductores de la empresa de transporte de carga “ISELT” de la ciudad de Arequipa, periodo 2018
Bibliographic record
Abstract
The objective of this research work is to determine the level of occupational risk factors that affect the drivers of the "ISELT" cargo transport company of the city of Arequipa, period 2018. The study is of a descriptive type, with a non-experimental, cross-sectional design and with a quantitative approach to data management. \nThe population is made up of 280 freight drivers who provide service to the company ISELT, in the city of Arequipa surveyed during the third quarter of the 2018 period. The sample consists of 105 freight drivers who provide service to the company, which was determined applying the technique of simple random sampling and applying the technique of correction by finitude. The technique used for the data collection was the survey and the instrument was the questionnaire of Occupational risk factors in transport \nEstablishing as a conclusion that: The factors of occupational risks presented by the drivers of the company ISELT are located preponderantly at a moderate level by 55%, followed by 35% at the low level and 10% at a very low level. Determining that there is a considerable rate of suffering a work accident, influenced by a set of factors that are part of the daily activities they perform.
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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 | 0.001 |
| 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) | 0.002 | 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".