Bacterial contamination of healthcare workers’ mobile phones in Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Mobile phones are potential reservoirs for pathogens and sources of healthcare-associated infections. More microbes can be found on a mobile phone than on a man's lavatory seat, the sole of a shoe, or a door handle. When examining patients, frequent handling of mobile phones can spread bacteria. Nevertheless, evidence of bacterial contamination of mobile phones used by healthcare workers in Africa was inconclusive. Thus, this meta-analysis and systematic review was conducted to estimate the pooled prevalence of bacterial contamination of mobile phones used by healthcare workers and the most frequent bacterial isolates in Africa. METHODS: We systematically retrieved relevant studies using PubMed/MEDLINE, POPLINE, HINARI, Science Direct, Cochrane Library databases, and Google Scholar from July 1, 2023 to August 08, 2023. We included observational studies that reported the prevalence of bacterial contamination of mobile phones among healthcare workers. The DerSimonian-random Laird's effect model was used to calculate effect estimates for the pooled prevalence of bacterial contamination in mobile phones and a 95% confidence interval (CI). RESULTS: = 97.9%, p value < 0.001). The most dominant type of bacteria isolated in this review was coagulase-negative staphylococci (CONS) which accounted for 44.0% of the pooled contamination rate of mobile phones used by healthcare workers, followed by Staphylococcus aureus (31.3%), and Escherichia coli (10.7%). CONCLUSIONS: In this review, the contamination of mobile phones used by HCWs with various bacterial isolates was shown to be considerable. The most prevalent bacteria isolates were coagulase-negative staphylococci, Staphylococcus aurous, and Escherichia coli. The prevalence of bacterial contamination in mobile phones varies by country and sub-region. Hence, healthcare planners and policymakers should establish norms to manage healthcare workers' hand hygiene and disinfection after using mobile phones.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.010 | 0.010 |
| 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".