Prevalence of Non-specific Low Back Pain Among Chinese Healthcare Workers (Surgeons and Surgical Nurses): A Multi-Center Survey Study
Bibliographic record
Abstract
Background: Low Back Pain (LBP) is one of the most common diseases affecting many persons including Healthcare Professionals (HCPs). LBP affects the quality of work and daily activities in HCPs. However, many factors contribute to LBP including age, Body Mass Index (BMI) and working experience. Purpose: The objective of this study was to assess the prevalence of LBP and examine the potential risk factors associated with LBP in Chinese HCPs (surgeons and surgical nurses). Method: A cross-sectional survey performed from October 2023 to January 2024. Quebec Back Pain Disability Scale (QBPDS) was used to assess the rating of LBP impairment. As well as Ovako Working Posture Assessment System (OWAS) is used to assess the working postures of HCPs. In addition to five more questions with yes/no answers. Results: A total of 250 surgeons and surgical nurses were collected from six hospitals with a mean age of 45.8 ± 11.0 years old, 47 females and 203 males. The BMI was 25.07 ± 1.49 kg, and working experience was 20.30 ± 10.0 years. Working hours in a week range from < 50 to > 55 hours. However, the OWAS action category was 1 in 109, 2 in 107, 3 in 30, and 4 in 4 participants. The majority of participants with mild LBP (90.4%), half of them had neck pain, and 26% with knee pain. The QBPDS scores of HCPs with LBP were 10.88 ± 4.78. Moreover, the risk factors associated with LBP include long working hours and incorrect postures of the trunk and lower extremities. As well as notice the orthopedic surgeon was the highest risk among HCPs (p = 0.046, 0.005). Conclusion: LBP is a common disorder in surgeons and surgical nurses and is associated with different factors including age, work experience, BMI, and abnormal working posture.
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 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 | 0.001 |
| 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) | 0.001 | 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".