ArticleQatar medical journal2025
Assessing the experience and attitude of emergency medical services staff toward linguistic diversity challenges in a Middle Eastern pre-hospital emergency care environment using machine learning analysis methods.
Article in Qatar medical journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Unveiling aphasia: Bilateral anterior temporal lobe atrophy mimicking psychosis.Qatar medical journal · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Language barriers significantly impact healthcare delivery, particularly in emergency medical services (EMS) operating in linguistically diverse environments. The demographic composition of Qatar, with its predominantly expatriate population, presents unique challenges for effective communication in pre-hospital care settings. The aim of this was to assess the opinions of personnel from the Hamad Medical Corporation Ambulance Service (HMCAS) regarding the impact of language barriers on pre-hospital emergency care. Methods: A cross-sectional study was conducted using an anonymous survey with a five-point Likert scale among 312 frontline personnel of HMCAS. Fisher's exact and Kruskal-Wallis tests were used to compare ordinal outcomes across groups. Machine learning algorithms, including ordinal logistic regression, support vector machines (SVM), and naive Bayes, were used to develop predictive models for HMCAS staff opinions on their language learning needs. Results: Both bivariate and multivariate analyses revealed significant differences in the frequency of experiencing communication challenges. The most influential factors identified were strong opinions on language barriers and the willingness of staff to enhance their language skills. Variables related to using family members as interpreters showed relatively low importance. The SVM model demonstrated the best predictive capability concerning staff perceptions about language learning needs, with an accuracy of 0.50 and an average area under the curve score of 0.74. Conclusion: Language barriers significantly impact pre-hospital emergency care in Qatar. The findings highlight the need for targeted interventions, such as language training programs and mobile translation apps. These strategies could enhance communication in multicultural EMS settings, improving patient care and reducing miscommunication risks. Future research should evaluate the long-term impact of these interventions on patient outcomes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.