Identificación de síntomas osteomusculares presentes en trabajadores de una empresa de consultaría en Barranquilla en el año 2017.
Bibliographic record
Abstract
Objective: To identify the musculoskeletal symptoms present in the workers of a consulting firm in the city of Barranquilla in 2017. Methodology: a quantitative cross-sectional study, in which 49 workers were surveyed through the Kuorinka Nordic Questionnaire, with the respective fulfillment of the inclusion criteria. Results The main problems encountered during the last three months were: discomfort of the back (high and low) occupying the first place with 71.42% followed by discomfort in the neck 61.22%, discomfort in the shoulders 36.73%, discomfort in the wrist 32.65%, discomfort In knee 26.53%, discomfort in elbow 10.2%, hip and thigh 8.16%. During the last 12 months, symptoms related to the back (high and low) were found in 44.9%, neck 22.45%, shoulders and wrist 12.24 and knees 10.2%. The average age of the population evaluated is 35 years, of which 73.47% are women and 26.56% are men. CONCLUSIONS Finally, back and neck pain were the most frequent musculoskeletal symptoms in the workers surveyed, which correlates with results obtained in other research carried out in different parts of the world and in other areas of the country. Musculoskeletal symptoms in countries such as the United States, Canada, Finland, Sweden and England generate more absenteeism and disability than any other group of diseases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".