What Do We Know About Interventions to Prevent Low Back Injury and Pain Among Nurses and Nursing Students? A Scoping Review
Bibliographic record
Abstract
STUDY BACKGROUND: Back injuries are common among nurses worldwide with lifetime prevalence of lower back pain ranging from 35% to 80%, making nursing a profession at great risk for back injuries. PURPOSE: This systematic scoping review explored and mapped existing evidence regarding the prevention of low back injury and pain among nurses and nursing students. METHODS: Using a scoping review methodology, six databases were searched initially in September 2017 and updated June 2020. Studies investigating interventions designed to reduce back injuries and pain among regulated nurses and student nurses, published in peer-review journals and written in English, were eligible for inclusion in this review. Quantitative, qualitative, and mixed methods studies of regulated nurses, nursing students, and nursing aides were included. Two independent reviewers screened, critically analysed studies using a quality appraisal tool, extracted data, and performed quality appraisals. RESULTS: Two searches yielded 3,079 abstracts and after title, abstract and screening, our final synthesis was based on 48 research studies. CONCLUSIONS: Forty years of research has demonstrated improvements in quality over time, the efficacy of interventions to prevent back injury and pain remains unclear, given the lack of high-quality studies. Further research, using multi-dimensional approaches and rigorous study designs, are needed.
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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 | 0.025 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".