Knowledge, Attitude and Perceptions of Healthcare Workers in Arab Countries Regarding Basic Life Support; a Systematic Review and Meta-Analysis.
Bibliographic record
Abstract
Introduction: Effective Basic Life Support (BLS) interventions, including cardiopulmonary resuscitation (CPR), are essential for enhancing survival rates. This review aimed to evaluate the knowledge, attitudes, and perceptions (KAP) of healthcare professionals regarding BLS in Arab countries. Methods: We conducted a systematic search on PubMed, Cochrane, Scopus, Web of Science, and EMBASE, to identify relevant studies. We included studies performed in Arab countries that included healthcare workers' KAP assessment towards BLS. The meta-analysis was carried out utilizing the OpenMeta Analyst Software, and a subgroup analysis was performed for Nursing staff category. The quality of the included cross-sectional studies was assessed through Newcastle-Ottawa quality assessment scale. Results: A total of 18 studies were included in our study, and eight of them entered the analysis. The study showed that 61.3% (95% confidence interval (CI): 48.9%, 73.7%, p<0.001) of health care workers were knowledgeable about the correct CPR ratio, and 62.1% (95% CI: 51.7%, 72.5%, p<0.001) answered the location of chest compression correctly. While, only 36.5% (95% CI: 23.5%, 49.6%, p<0.001) had correct answers regarding the compression rate, 48.1% (95% CI: 38.1%, 58.0%, p<0.001) were aware of the compression depth, and 34.8% (95% CI: 22.9%, 46.7%, p<0.001) answered the sequence correctly. Conclusion: The study revealed a gap regarding the BLS KAP of healthcare workers in different Arab countries, which crucially requires taking actions, in terms of frequent certified training sessions, assessments, and clear protocols.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".