Current state of knowledge of basic life support in health professionals of the largest city in Pakistan: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Basic Life Support (BLS) is the recognition of sudden cardiac arrest and activation of the emergency response system, followed by resuscitation, and rapid defibrillation. According to WHO, Pakistan has one of the highest mortality rates from accidental deaths therefore assessment and comparison of BLS knowledge in health professionals is crucial. We thereby aim to assess and compare the knowledge of BLS in doctors, dentists and nurses. METHODS: A multi-centric cross-sectional survey was conducted in Karachi at different institutions belonging to the private as well as government sector from January to March 2018. We used a structured questionnaire which was adapted from pretested questionnaires that have been used previously in similar studies. Descriptive statistics were analyzed using SPSS v22.0, where adequate knowledge was taken as a score of at least 50%. P < 0.05 was considered as significant. Logistic regression was used to identify the factors affecting the knowledge regarding BLS in health care professionals. RESULTS: The responders consisted of 140 doctors, nurses and dentists each. Only one individual (dentist) received a full score of 100%. In total, 58.3% of the population had inadequate knowledge. Average scores of doctors, dentists and nurses were 53.5, 43.3 and 38.4% respectively. Doctors, participants with prior training in BLS and those with 6 to 10 years after graduation were found to be a significant predictor of adequate knowledge, on multivariate analysis. CONCLUSION: Even though knowledge of BLS in doctors is better than that of dentists and nurses, overall knowledge of health care professionals is extremely poor. Present study highlights the need for a structured training of BLS for health care workers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".