Prevalence and predictors of work-related musculoskeletal disorders among healthcare professionals in Sub-Saharan African region: systematic review and meta-analysis protocol
Bibliographic record
Abstract
Abstract Background: The Global Burden of Diseases 2017 found that musculoskeletal disorders (MSDs) are the second most prevalent cause of years lost to injury, although years of life lost are decreasing in low-income countries, especially in Sub-Saharan Africa. The objective will be to describe the regional prevalence of WMSD for different anatomical body areas and their risk factors in different health professions in the sub-Saharan African region. Method: We will search databases such as Scopus, PubMed, Science Direct, AJOL, and Google Scholar for publications published between January 2002 and December 2022. The primary outcome will be the prevalence of work-related musculoskeletal disorders among health professionals, and risk factors related to WRMSDs will be the secondary outcome. Three reviewers will screen all abstract data, full-text articles, and all citations independently. The Newcastle-Ottawa scale (NOS) will be used to assess the quality of eligible publications. Subgroup analysis will be conducted to explore the potential heterogeneity (e.g., age, sample size, gender, and occupational activities). Publication bias and heterogeneity will be assessed and reported using the appropriate tools. Discussion: This systematic review and meta-analysis will provide a synthesis of the literature on work-related musculoskeletal disorders and their predictors among health professionals in the Sub-Saharan Africa region. The consensus of data from this review will provide a regional view to help occupational health-related policymakers, healthcare professionals, and program managers in developing countries gain a better understanding of the prevalence, causes, and trends to build better evidence-based occupational musculoskeletal health and disorders prevention programs among various health professionals. Systematic review registration: PROSPERO, CRD42023455517
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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 | 0.052 | 0.083 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.022 | 0.026 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.041 | 0.004 |
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".