Low back pain in beauty salons professionals in the city of Fortaleza-CE
Bibliographic record
Abstract
Introduction: Low back pain can be defined as pain below the ribs and above the upper gluteal line. Objectives: The study aimed to analyze low back pain in professionals from beauty salons in the city of Fortaleza, state of Ceará. Methods: Descriptive, quantitative-qualitative, transversal, non-probabilistic research in the snowball modality, conducted between June and August 2021 in the José Walter neighborhood. Two sociodemographic questionnaires and the Quebec Back Pain Disability scale were applied, which seeks to assess how pain affects the participants' daily lives. Results: Forty-two professionals were interviewed, of which 32 women (76.2%), with a mean age of 39.45 ± 10.99 years. Women were more likely to have an onset of low back pain and to live with pain for a longer time compared to men, in addition to these professionals having a significant overload for the hours worked. 52% of respondents showed significant clinical changes, mainly in relation to stand up for 20-30 minutes (16.7%), sit in a chair for several hours (14.3%), walk several kilometers (19%), carry two bags with groceries (14.3%) and lift and carry a heavy suitcase (28.6%). Conclusions: It was evidenced that low back pain may be related to personal or environmental factors, with a sedentary lifestyle, length of service and working hours as strong indications for the onset of low back pain, with impairment in daily tasks.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".