Low back pain among bodybuilding professors of the West zone of the city of Rio de Janeiro
Bibliographic record
Abstract
ABSTRACT BACKGROUND AND OBJECTIVES: Low back pain is one of the most common musculoskeletal symptoms in industrialized societies, according to the World Health Organization. This study aimed at investigating the prevalence of low back pain among bodybuilding professors of fitness centers of the city of Rio de Janeiro and at observing correlations between age, working time, working hours and low back pain intensity. METHODS: The adapted questionnaire of the Quebec Pain Disability Scale was applied to 50 physical education professors of both genders (age = 31.86±6.86 years) working with bodybuilding in fitness centers, with minimum weekly working hours of 12h, and at least three years acting in the area. This was a survey-type descriptive cross-sectional study. RESULTS: From 50 interviewed professors, 62% have stated not feeling any type of lumbar discomfort, while just 38% have stated feeling some type of pain. From these, 20% have stated feeling daily pain, 6% weekly and 12% have reported monthly pain. About pain intensity in its worst moment, 14% have stated it is mild, 20% moderate and just 6% have reported severe pain. There has been positive and significant correlation (p<0.05) between age and working time and between working time and low back pain intensity. CONCLUSION: Low back pain prevalence was not high among interviewed professionals. Results show that older individuals working for a longer time are those with more severe low back pain.
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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 | 0.001 | 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) | 0.003 | 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".