Knowledge on Stroke Recognition and Management Among Emergency Department Healthcare Professionals in the Republic of Cyprus
Bibliographic record
Abstract
Data show that ischemic stroke is one of the leading causes of death and disability worldwide. Knowledge of ischemic stroke-related guidelines is vital for health care professionals working in the Emergency Departments (EDs) because it affects the early diagnosis and timely treatment to combating stroke. We aimed to explore knowledge regarding the recognition and management of the ischemic stroke among Greek-Cypriot emergency health care personnel (nurses and physicians). A descriptive cross-sectional correlation study was implemented from November 2019 to April 2020 across 4 private and 7 public EDs in Cyprus. Data were collected with the use of a self-reported questionnaire, developed by the research team. 255 nurses [Response Rate (RR): 74.1%] and 26 physicians (RR; 47.3%) completed the questionnaire. The participants gave a correct answer to an average of 12.9 statements from a total of 28 (SD: 4.2) with nurses and physicians scoring a mean of 12.9 (SD:4.1) and 15,7 (SD: 4) respectively. Participating hospitals scored an average of 10.3 to 14.1. Participants with previous training scored an average of 1.45 addi-tional correct answers. Greek-Cypriot health care professionals in EDs reported poor to moderate knowledge about ischemic stroke highlighting the need for targeted and continuous education, and further study of factors related with this may be of interest. Also, development and implementation of evidence-based protocols and enhanced education regarding ischemic stroke should be considered essential interventions for emergency health care professionals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".