Nurses knowledge, awareness and confidence level to recognize stroke symptoms specific to women: A cross-sectional study
Bibliographic record
Abstract
Background: Stroke remains the third-leading cause of death and a major cause of disability in women. Men and women share common stroke symptoms. However, women can also experience “specific,” or “atypical” stroke symptoms. Early recognition of stroke and recognizing sex race-ethnic differences in stroke symptoms is crucial to better outcomes. Nurses play a crucial role in identifying stroke symptoms and activating care early. However, the general nurse’s knowledge and confidence level to recognize stroke in women is unknown. This study aimed to provide insight into nurse’s awareness, knowledge and confidence to recognize women's specific stroke symptoms.Methods: A cross-sectional study was conducted over four months. 129 nurses were recruited via targeted social media platforms to complete an online survey. Data were analyzed using descriptive statistics, independent t-tests, and one-way ANOVA.Results: Nurses were 86% women, and 55% held advanced degrees. Over 80% identified the common stroke symptoms; over 70% identified the additional stroke symptoms. Less than 25% could identify specific stroke symptoms in women. A majority of the nurses (76%) lacked the confidence to recognize stroke symptoms in women, with confidence scores statistically lower in advanced level trained nurses.Conclusions: The majority of nurses know and feel confident that they could identify the most common and additional stroke symptoms. However, there are knowledge deficits, and nurses lack confidence in recognizing stroke symptoms in women. Education targeted at nurses should include strategies to enhance their awareness, knowledge and confidence level in recognizing stroke symptoms specific to women.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".