The global prevalence of overweight and obesity among nurses: A systematic review and meta‐analyses
Bibliographic record
Abstract
Abstract Background Several studies have reported the prevalence of overweight and obesity in various countries but the global prevalence of nurses with overweight and obesity remains unclear. A consolidation of figures globally can help stakeholders worldwide improve workforce development and healthcare service delivery. Objective To investigate the global prevalence of overweight and obesity among nurses. Design Systematic review with meta‐analysis. Setting 29 different countries across the WHO‐classified geographical region. Participants Nurses. Methods Eight electronic databases were searched for articles published from inception to January 2023. Two independent reviewers performed the article screening, methodological appraisal and data extraction. Methodological appraisal was conducted using Newcastle‐Ottawa Scale (NOS). Inter‐rater agreement was measured using Cohen's Kappa. Meta‐analyses were conducted to pool the effect sizes on overweight, obesity and waist circumference using random effects model and adjusted using generalised linear mixed models and Hartung–Knapp method. Logit transformation was employed to stabilise the prevalence variance. Subgroup analyses were performed based on methodological quality and geographical regions. Heterogeneity was assessed using the I2 statistic. Results Among 10,587 studies, 83 studies representing 158,775 nurses across 29 countries were included. Based on BMI, the global prevalence of overweight and obesity were 31.2% (n = 55, 95% CI: 29%–33.5%; p < .01) and 16.3% (n = 76, 95% CI: 13.7%–19.3%, p < .01), respectively. Subgroup analyses indicated that the highest prevalence of overweight was in Eastern Mediterranean (n = 9, 37.2%, 95% CI: 33.1%–41.4%) and that of obesity was in South‐East Asia (n = 5, 26.4%, 95% CI: 5.3%–69.9%). NOS classification, NOS scores, sample size and the year of data collected were not significant moderators. Conclusions This review indicated the global prevalence of overweight and obesity among nurses along with the differences between regions. Healthcare organisations and policymakers should appreciate this increased risk and improve working conditions and environments for nurses to better maintain their metabolic health. Patient or Public Contribution Not applicable as this is a systematic review. Registration PROSPERO (ref: CRD42023403785) https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=403785 . Tweetable Abstract High prevalence of overweight and obesity among nurses worldwide.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".