Willingness and self-confidence of healthcare workers in Bahrain in assisting with in-flight emergencies
Bibliographic record
Abstract
Abstract: BACKGROUND: In-flight medical emergency (IFE) impose considerable challenges on healthcare workers (HCWs) because of limited resources, constrained environment, and medico-legal issues. This study assessed HCWs knowledge, willingness, and confidence in addressing in-flight medical emergencies. MATERIALS AND METHODS: A cross-sectional study was conducted between June and August 2023 among nurses and physicians working in primary healthcare centers and governmental hospitals in Bahrain. Subjects were selected using stratified random sampling; a self-administered online questionnaire of high reliability (Cronbach alpha = 0.914) was used to collect the data. Logistic regression analysis were performed to determine association of knowledge, willingness, and confidence in dealing with in-flight emergencies with various characteristics of HCWs. RESULTS: The study included 805 HCWs with mean age of 35.5 years (SD=9.2). The findings indicated deficiency in training, with <10% of participants trained on IFE. A considerable proportion of participants exhibited low levels of knowledge (88.3%) and confidence (75.9%) with IFE. Nonetheless, more than half of the participants expressed the willingness to assist in IFE (59.1%). Non-Bahraini healthcare professionals (odds ratio [OR] = 2.901, P < 0.001) had higher knowledge of IFE. Nurses (OR = 1.642, P = 0.047) and participants with longer work experience had higher willingness to assist in IFE. In addition, professionals who were non-Bahraini (OR = 3.249, P < 0.001), working in secondary care (OR = 1.619, 95% confidence interval P = 0.021), had had training on IFE (OR = 2.247, P = 0.004), and had encountered IFE before (OR = 1.974, P = 0.006) had greater self-confidence levels. CONCLUSION: Considering the low levels of knowledge and confidence healthcare professionals in Bahrain had with regard to IFE, targeted training initiatives and educational programs are necessary to improve HCW’s confidence and preparedness to deal with such emergencies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".