Back

Magnitude of work-related musculoskeletal disorders and ergonomic risk practices among medical laboratory professionals in Northwest Ethiopia: A cross-sectional study.

Alibsew, M. T.; Shiferaw, M. B.; Molla, H.; Misganaw, A. S.

2023-01-09 occupational and environmental health
10.1101/2023.01.09.23284343 medRxiv
Show abstract

ObjectiveThis study aimed to assess the magnitude of work-related musculoskeletal disorders (WMSDs) and ergonomic risk practices among medical laboratory professionals in North West Ethiopia. DesignFacility-based cross-sectional study MethodsCross-sectional study design was employed among medical laboratory professionals (MLPs). The Nordic musculoskeletal questionnaire was adopted and used. In addition to questionnaires about socio-demographic characteristics and ergonomic risk practice, one-to-one interviews, and a direct observational checklist were used. Data was entered into Epi Data 3.1 and then exported and analyzed using SPSS version 25.0. Logistic regression analysis was used to estimate the 95% CI (AOR) at a cut-off value of p <0.05 for statistically significant tests. ResultsA total of 238 MLPs participated in the study. The magnitude of WMSDs was 116(48.7%). The most affected body parts were the lower back (20.6%) and wrists (16.4%). The magnitude of WMSDs among government-owned hospitals was the highest (56.4%). 67.6% MLPs never heard about ergonomics. The general mean score of workstations was 2.28. Ergonomic risk practices like repetitive movement and doing of high workload were significantly associated with WMSDs. ConclusionThe current findings revealed a high magnitude of WMSDs that strongly need applying preventive action before body symptoms developed. Improving and renovating workplace design and enhancing awareness of MLPs were the necessary measures to control ergonomic risk factors.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.