A Multidisciplinary Workplace Intervention for Chronic Low Back Pain among Nursing Assistants in Iran
Bibliographic record
Abstract
Study Design Interventional research with a 6-month follow-up period. Purpose We aimed to establish the effectiveness of a multidisciplinary workplace intervention on reduction of work-related low back pain (WRLBP), using ergonomic posture training coupled with an educational program based on social cognitive theory. Overview of Literature WRLBP is a major occupational problem among healthcare workers, who are often required to lift heavy loads. Patient handling is a particular requirement of nurse aides, and has been reported as the main cause of chronic WRLBP. Methods We included 125 nursing assistants from two hospitals affiliated to Qom University of Medical Sciences from May to December 2015. There was an intervention hospital with a number of 63 nursing assistants who received four multidisciplinary educational sessions for 2 hours each plus ergonomic posture training over two days and a control hospital with a number of 62 nursing assistants who didn't receive educational intervention about low back pain. The outcomes of interest were reductions in WRLBP intensity and disability from baseline to the follow up at 6 months, which were measured using a visual analog scale and the Quebec Disability Scale. Descriptive and analytical statistics were used to analyze the data. Results The comparison tests showed significant change from baseline in reduction of WRLBP intensity following the multidisciplinary program, with scores of 5.01±1.97 to 3.42±2.53 after 6 months on the visual analog scale in the intervention group (p<0.001) and no significant change in control groups. There was no significant difference in the disability scores between the two groups (p=0.07). Conclusions We showed that our multidisciplinary intervention could reduce the intensity of WRLBP among nurse aides, making them suitable for implementation in programs to improve WRLBP among nursing assistants working in hospitals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".