Conectar
To identify factors that influence the development of patient safety culture among nursing students.
Integrative review.
A comprehensive literature search for publications from 2004 to 2024 was conducted using PubMed, LIVIVO, CINAHL, SCOPUS, and ERIC. A summarising content analysis was performed on 47 articles.
Students value patient safety but need guidance, supervision, structured education, supportive environments, interdisciplinary curricula, simulation training, and error-reporting training. Teamwork fosters learning, but hierarchical cultures, poor mentorship, unclear roles, stress, negative experiences, and bullying hinder communication and students' willingness to speak up. Emotions, identity, socialisation, and resilience shape students' safety practices, confidence, and advocacy.
Enhancing nursing education, clinical environments, and policies is vital to patient safety practices among student nurses. Integrating comprehensive patient safety education, reflective learning, and structured transition programmes can bridge gaps between theory and practice, fostering critical thinking and confidence. Cultivating non-punitive cultures and collaboration across institutions and professions ensures learning, mutual support, and safer care delivery, with future research needed to assess long-term patient safety culture development.
No comprehensive review has yet examined all factors influencing the development of patient safety culture in undergraduate nursing students. This review addresses this gap. Understanding these factors can foster a sustainable safety culture, reduce student stress, and guide improvements in education and clinical practice. Inadequate safety integration into curricula, hierarchical dynamics, and mentorship gaps risk undermining patient safety.
By synthesising evidence from multiple studies, it yields comprehensive insights for both educational and clinical settings. The findings have important implications for educators, policymakers, and healthcare organisations, guiding improvements in curricula, teaching methods, and clinical learning environments to foster a robust patient safety culture from the beginning of training.
This study followed EQUATOR and PRISMA reporting guidelines for systematic reviews.
No patient or public involvement.
To assess the capability of a convolutional neural network trained by transfer learning on anterior segment optical coherence tomography (AS-OCT) images, Placido-disk corneal topography images and external photographs to predict age and biological sex.
Development of a deep learning model trained on retrospectively collected data using transfer learning.
A multicentre secondary care public health trust based in London.
We included 557,468 scans from 40,592 eyes of 20,542 patients. Data were extracted from all patients who underwent MS-39 imaging within our trust from October 2020 to March 2023.
Primary outcome measures for biological sex classification included accuracy, precision, recall, F1-score and area under the receiver operating curve (ROC-AUC). Primary outcome measures for age prediction were Pearson correlation coefficients (r), coefficients of determination (R²) and the mean absolute error (MAE) to evaluate the predictive performance. The secondary outcome was to visualise and interpret the model’s decision-making process through the construction of saliency maps.
For age prediction, the MAEs for the Placido, AS-OCT and external photograph models were 5.2, 5.1 and 6.2 years, respectively. For gender classification, the same models achieved ROC-AUCs of 0.88, 0.73 and 0.81, respectively. No difference in performance was found in the analysis of corneas with pathological topography. The saliency maps highlighted the peri-limbal cornea for age prediction and the central cornea for gender discrimination.
Our study demonstrates that deep learning models can extract age and gender information from anterior segment images. These findings support the concept that the anterior segment, like the retina, encodes important biological information. Future research should explore whether these models can predict specific systemic conditions.
by Drew Gorenz, Norbert Schwarz
People hear jokes live and pre-recorded in a variety of settings, from comedy clubs, bars, outdoor venues, cafes, to their own home or car. While a lot of research has analyzed the significance of the content of jokes, we know less about the significance of the setting one hears them in. Some settings can have interfering background noise or poor acoustics, reducing an audience’s ease of processing heard jokes. Would this affect how funny the jokes seem? Two experiments with audio clips of stand-up comedy performances show that participants found jokes less funny when background noise interfered with their listening.Intrathoracic cancers, such as lung cancer, mesothelioma and thymoma, represent diagnostic challenges in primary care. We aimed to summarise evidence on the performance of imaging techniques that could aid the detection of intrathoracic cancers in low prevalence settings.
Systematic review and quality appraisal using Quality Assessment of Diagnostic Accuracy Studies-2 and Grading of Recommendations Assessment, Development and Evaluation.
MEDLINE, Embase and Web of Science were searched with a predesigned search strategy for articles from January 2000 to January 2024.
We included studies relevant for primary care, where participants were suspected of having intrathoracic cancer and reported on at least one diagnostic performance measure. We excluded studies where the cancer diagnosis was already established. Data extraction and synthesis screening were conducted independently by two reviewers. Data extraction and quality appraisal were conducted by one reviewer and checked by a second reviewer.
Out of 30 539 records identified by the database searches, 13 studies were included. There was heterogeneity in the types of cancers, populations included and reported diagnosis pathways for suspected cancers. Imaging modalities investigated included chest X-ray (three studies), computer tomography (CT, six studies), magnetic resonance imaging (two studies), positron emission tomography CT (two studies), ultrasound (two studies) and scintigraphy (one study). Chest X-ray sensitivity reported for lung cancer ranged from 33.3% to 75.9%, with specificity ranging from 83.2% to 95.5%. For CT, reported sensitivity varied from 58% for pleural malignancy to 100% for lung cancer. One study investigating an artificial intelligence tool to detect lung cancer found poor detection performance in a real-world patient cohort.
We found a limited number of studies reporting on the diagnostic performance of usual imaging techniques when used in unselected primary care settings for the diagnosis of intrathoracic cancer in symptomatic patients. There is a need for more studies evaluating such techniques in the general population presenting in primary care, where the prevalence is relatively low. A better understanding of the performance could lead to better detection strategies for intrathoracic cancers in primary care. Intrathoracic cancers, such as lung cancer, mesothelioma and thymoma, represent diagnostic challenges in primary care. We aimed to summarise evidence on the performance of imaging techniques that could aid the detection of intrathoracic cancers in low prevalence settings.
FreshRSS 