Prevalence of musculoskeletal pain among professional drivers: A systematic review
Bibliographic record
Abstract
OBJECTIVES: Professional drivers are at high risk of developing musculoskeletal pain (MSP) due to risk factors such as prolonged sitting, whole body vibration, awkward posture, and repetitive actions. This review investigates the reported prevalence of MSP among professional drivers. METHODS: An electronic search of Medline (1946 + via OvidSP), Embase (1974 + OvidSP), CINAHL (1982+), AMED, PubMed, and Web of Science from 1990 to July 2019 was performed. Methodological quality of studies was assessed using three quality assessment tools for cross-sectional, case-control, and prospective cohort studies. The prevalence of MSP was reported using descriptive analysis. RESULTS: A total of 56 studies conducted in 23 different countries across a total of 14 types of occupational transport were reviewed. Data of a total pooled population of 18 882 professional drivers were analyzed for MSP. The prevalence of MSP ranged between 43.1% and 93%. The low back was the most frequently reported body region for MSP with a meta-prevalence rate of 53% (N = 9998). Neck, shoulder, and upper back were the other common regions with high prevalence. CONCLUSION: There is a high prevalence of MSP in professional drivers and low back was the most frequently reported body region, followed by neck, upper back, shoulder, knee, hip/thigh, wrist, ankle, and elbow. MSP is complicated in nature and therefore in-depth exploration of causal relationships between MSP and risk factors is necessary so that appropriate healthcare programs can be initiated to prevent and treat MSP effectively.
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".