formation continue_articles

Nurses' learning about professional interpersonal communication: Findings from an integrative review.

Tue, 01 Jul 2025 04:00:00 +0000

Nurse Education Today; 07/01/2025
(AN 184345965); ISSN: 02606917
CINAHL Complete

Student nurses' satisfaction and confidence: Blended learning skills training on satellite campuses.

Tue, 01 Jul 2025 04:00:00 +0000

Teaching & Learning in Nursing; 07/01/2025
(AN 185650911); ISSN: 15573087
CINAHL Complete

A flourishing-centered curriculum can only thrive through the embrace of diversity...Maurana CA, Fritz JD, Witten AA, et al. Advancing flourishing as the north star of medical education: A call for personal and professional development as key to becoming

Tue, 01 Jul 2025 04:00:00 +0000

Medical Teacher; 07/01/2025
(AN 186012919); ISSN: 0142159X
CINAHL Complete

Navigating the learning curve: Advice for newly licensed nurses.

Tue, 01 Jul 2025 04:00:00 +0000

Nursing; 07/01/2025
(AN 186110768); ISSN: 03604039
CINAHL Complete

Inspiring, learning, leading: a celebration of practice nurses.

Tue, 01 Jul 2025 04:00:00 +0000

Practice Nursing; 07/01/2025
(AN 186155665); ISSN: 09649271
CINAHL Complete

Nurse educators' challenges of problem-based learning implementation at Ethiopian public universities: A phenomenological qualitative study

Sat, 28 Jun 2025 08:22:52 +0000

Author Names: Bolado G.N.,Ataro B.A.,Kebamo T.E.,Ayana A.S.,Minuta W.M.

Database Source: Embase Weekly Updates

Journal Title: PLOS ONE

Article Title: Nurse educators' challenges of problem-based learning implementation at Ethiopian public universities: A phenomenological qualitative study

Year: 2025

Issue: 6 June

Volume: 20

Abstract: Background Nursing students are the upcoming healthcare workforce who learn to face the reality of their roles and work in clinical practice, knowing all aspects of patient care and contributing their knowledge, attitude, and practice to the healthcare system in the future. Problem-based learning is one of the strategies to identify and encourage their theoretical, practical, and clinical thinking abilities. Still, its implementation is hindered by different shortcomings in developing countries, especially in Ethiopia. Objective To explore the nurse educators' challenges with problem-based learning implementation at Ethiopian public universities, 2023. Method A phenomenological qualitative study design approach was used. Eighteen nurse educators were selected purposively from ten universities to explore the challenges they encountered while implementing problem-based learning. Data were collected using in-depth interviews, and field notes were organized from August 01-October 01, 2023. The data analysis was done by OpenCode software and conventional content analysis was carried out following Colaizzi's 7-step approach. Results In the study, four major themes and eighteen subthemes emerged, covering challenges encountered by nurse educators in Ethiopian public universities: challenges about educators themselves, nursing students, institutions/universities, problem-based learning strategies. Some specific points raised include lack of materials, negative student perceptions of problem-based learning, absence of standardized cases, and subjective assessments. Conclusion Nurse educators at public universities in Ethiopia faced challenges related to themselves, nursing students, their respective universities, problem-based learning cases, and assessment methods during problem-based learning implementation. Understanding these points provide further insights into the specific difficulties encountered by nurse educators concerning problem-based learning implementation. Copyright © 2025 Bolado et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Role of nurses in moderating the emotional dynamics in the clinical learning environments: Implications for medical students' experience

Sat, 28 Jun 2025 08:22:52 +0000

Author Names: Gupta S.,Howden S.,Moffat M.,Pope L.,Kennedy C.

Database Source: Embase Weekly Updates

Journal Title: Medical education

Article Title: Role of nurses in moderating the emotional dynamics in the clinical learning environments: Implications for medical students' experience

Year: 2025

Issue:

Volume:

Abstract: INTRODUCTION: Existing literature recognises that health professionals' socialisation in the workplace involves an emotional component, including management of feelings as per professional expectations and demands. However, there is limited understanding of the emotions-related processes involved in the interprofessional educational space of the clinical learning environment (CLE) and the role of nurses in moderating the emotional dynamics. This paper explores ways in which emotions operate in the interprofessional encounters of the CLE, utilising Hochschild's theory on emotional work. METHOD(S): An ethnographic approach was adopted that included 120 hours of observations conducted in two hospital wards hosting clinical placements for medical students and 36 individual interviews with staff and students populating these clinical sites. Key themes were derived using the circular process of ethnographic data analysis utilising the sensitising concepts of emotional labour, emotional gifts and feeling rules, together with the relational dimensions of interprofessional interactions in the CLE. RESULT(S): The following key themes were identified: emotional gifts from nurses to medical students and junior doctors, interprofessional interactions suggesting a breach in feeling rules and suspension of interprofessional hierarchies that engendered goodwill and appreciation among health care teams in the CLE. Interconnected subthemes highlight the nurses' efforts to maintain the sentimental order in the CLE through generously gifting emotional support to students and doctors in a variety of situations, impacting their professional socialisation and well-being. However, workplace stresses such as time pressures and staff shortages created interprofessional tensions, generating negative emotions. Blurred professional boundaries and professional humility positively impacted interprofessional dynamics and student experience in the CLE. DISCUSSION(S): This ethnographic exploration of the CLE revealed the emotions-related processes nested within the interprofessional space of health care practice. In their work towards emotional housekeeping of the CLE, the nurses offer emotional gifts to medical staff and students populating the ward. Workload issues and inadequate staffing influence both cognition and behaviour adversely, impacting interprofessional dynamics. Our findings support fostering professional humility as a pedagogical tool so that we collectively move away from current structures that keep us siloed. Copyright © 2025 The Author(s). Medical Education published by Association for the Study of Medical Education and John Wiley & Sons Ltd.

Deep Learning-Based Decision Support System for Nurse Staff in Hospitals

Sat, 28 Jun 2025 08:22:52 +0000

Author Names: Chen J.,He F.,Tang L.,Gu L.

Database Source: Embase Weekly Updates

Journal Title: Big data

Article Title: Deep Learning-Based Decision Support System for Nurse Staff in Hospitals

Year: 2025

Issue:

Volume:

Abstract: To promote the informatization management of hospital human resources and advance the application of hospital information technology. The application of deep learning (DL) technologies in health care, particularly in hospital settings, has shown significant promise in enhancing decision-making processes for nurse staff. Utilizing a hospital management decision support system based on data warehouse theory and business intelligence technology to achieve multidimensional analysis and display of data. This research explores the development and implementation of a DL-Based Clinical Decision Support System (DL-CDSS) tailored for nurses in hospitals. DL-CDSS utilizes advanced neural network architectures to analyze complex clinical data, including patient records, vital signs, and diagnostic reports, aiming to assist nurses in making informed decisions regarding patient care. By leveraging large-scale datasets from Hospital Information Systems, DL-CDSS provides real-time recommendations for treatment plans, medication administration, and patient monitoring. The system's effectiveness is demonstrated through improved accuracy in clinical decision-making, reduction in medication errors, and optimized workflow efficiency. The system analyzes and displays nurses data from hospitals in terms of quantity, distribution, structure, forecasting, analysis reports, and peer comparisons, providing head nurses with multilevel, multiperspective data mining analysis results. Challenges such as data integration, model interpretability, and user interface design are addressed to ensure seamless integration into nursing practice, also concludes with insights into the potential benefits of DL-CDSS in promoting patient safety, enhancing health care quality, and supporting nursing professionals in delivering optimal care.

Prediction of Job Burnout in Nurses Based on the Job Demands-Resources Model: An Explainable Machine Learning Approach

Sat, 28 Jun 2025 08:22:52 +0000

Author Names: Zeng Y.,Zhao X.,Xie Z.,Lin X.,Qi M.,Li P.

Database Source: Embase Weekly Updates

Journal Title: Journal of advanced nursing

Article Title: Prediction of Job Burnout in Nurses Based on the Job Demands-Resources Model: An Explainable Machine Learning Approach

Year: 2025

Issue:

Volume:

Abstract: AIM: To combine the Job Demand-Resource (JD-R) model with machine learning (ML) techniques to identify the key factors affecting job burnout (JB) among Chinese nurses. DESIGN: A Cross-Sectional Study. METHOD(S): This study utilised a stratified sampling method to recruit 3449 eligible nurses from eight cities in Shandong Province between June and December 2021. After data cleaning, 2998 valid samples were retained. The dataset was randomly split into a training set (75%) and a test set (25%). The Boruta algorithm was used to select relevant variables for model construction. Six-millilitre models were compared using cross-validation, with mean absolute error (MAE), root mean square error (RMSE) and R-squared (R2) used to select the best model. The Shapley Additive Explanation (SHAP) method was used to identify key predictors of JB. RESULT(S): The average JB score among nurses was (32.88 +/- 11.45). Among the 20 variables, 17 were identified by the Boruta algorithm as strongly associated with JB, including 7 job demand-related variables and 10 job resource-related variables. After comparing 6-ml models, the Random Forest was identified as the optimal model (MAE = 6.56, RMSE = 8.86, R2 = 0.63). SHAP analysis further revealed the importance ranking of these 17 variables and identified four key predictors: psychological distress (SHAP = 4.07), perceived organisational support (SHAP = 2.03), emotional intelligence (SHAP = 1.81) and D-type personality (SHAP = 1.73). CONCLUSION(S): By integrating the JD-R model framework, ML algorithms proved effective in identifying critical predictors of nurses' JB. SHAP analysis identified four primary determinants: psychological distress, perceived organisational support, emotional intelligence and D-type personality. These findings provide novel insights for nursing administrators to optimise intervention strategies. IMPACT: Not applicable. PATIENT OR PUBLIC INVOLVEMENT: This study did not include patient or public involvement in its design, conduct or reporting. Copyright © 2025 John Wiley & Sons Ltd.

Deep-learning-based software versus experienced nuclear medicine physician to read [¹⁸F]-piflufolastat used for PSMA-PET/CT imaging in prostate cancer patients presenting with first biochemical recurrence: A comparative study of detection rate

Sat, 28 Jun 2025 08:22:52 +0000

Author Names: Gauthe M.,Balogova S.

Database Source: Embase Weekly Updates

Journal Title: European Journal of Nuclear Medicine and Molecular Imaging

Article Title: Deep-learning-based software versus experienced nuclear medicine physician to read [18F]-piflufolastat used for PSMA-PET/CT imaging in prostate cancer patients presenting with first biochemical recurrence: A comparative study of detection rate

Year: 2025

Issue:

Volume:

Abstract: This study evaluated whether a CE-marked deep-learning-based web platform (PSMA-DLwp) maintains a per-patient detection rate (DR) comparable to that of an experienced nuclear medicine physician (ENMP) for identifying prostate cancer (PCa) foci using [18F]-piflufolastat PET/CT in patients with first biochemical recurrence. Secondary objectives included per-region DR and the correlation of SUVmax and tumor burden assessments between the ENMP and PSMA-DLwp. Method(s): A retrospective analysis was conducted using data from the phase III PYTHON study, which compared [18F]-piflufolastat PET/CT to [18F]-fluoromethylcholine PET/CT in patients with first biochemical recurrence of PCa. Both ENMP and PSMA-DLwp independently reviewed [18F]-piflufolastat PET/CT scans. Concordance in DRs, regional analysis, and correlation of semi-quantitative parameters (SUVmax, tumor burden) were assessed. Discordant cases were re-evaluated by the ENMP to assess potential improvements provided by PSMA-DLwp. Result(s): Out of 192 evaluable scans, per-patient DRs were concordant in 75% of cases, with no statistically significant difference between ENMP and PSMA-DLwp (p = 0.11). Per-region analysis for T and N staging showed no significant DR differences (p = 0.72 and p = 0.30, respectively). PSMA-DLwp identified more foci in distant regions (M1a, M1b), but these were not statistically significant. Strong positive correlations were observed between ENMP and PSMA-DLwp for SUVmax (<inf>s</inf>=0.85; p < 0.0001) and tumor burden (<inf>s</inf>=0.89; p < 0.0001). In 8% of discordant cases, PSMA-DLwp improved the ENMP's initial assessment. Conclusion(s): The PSMA-DLwp achieves DRs comparable to an expert reader and reliably quantifies tumor burden and SUVmax, supporting its use as an adjunct in the interpretation of [18F]-piflufolastat PET/CT for PCa. Its integration into clinical workflows can enhance diagnostic consistency and efficiency, particularly in high-volume or complex cases, thereby supporting more informed clinical decision-making. Copyright © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

Reconceptualising interprofessional learning: A cross-cultural and religious perspective on physician development

Sat, 28 Jun 2025 08:22:52 +0000

Author Names: Taufiqurrahman M.,Afriani Z.L.,Eliya I.,Yusrizal M.

Database Source: Embase Weekly Updates

Journal Title: Medical education

Article Title: Reconceptualising interprofessional learning: A cross-cultural and religious perspective on physician development

Year: 2025

Issue:

Volume:

Abstract:

ABS0163 PREDICTION OF RHEUMATOLOGICAL DISEASES USING MACHINE LEARNING MODELS AND NATURAL LANGUAGE PROCESSING TECHNIQUES ON PHYSICIAN NOTES

Sat, 28 Jun 2025 08:22:52 +0000

Author Names: Genc A.C.,Turkoglu F.,Bilgin E.,Gonullu E.

Database Source: Embase Weekly Updates

Journal Title: Annals of the Rheumatic Diseases

Article Title: ABS0163 PREDICTION OF RHEUMATOLOGICAL DISEASES USING MACHINE LEARNING MODELS AND NATURAL LANGUAGE PROCESSING TECHNIQUES ON PHYSICIAN NOTES

Year: 2025

Issue: Supplement 1

Volume: 84

Abstract: Background: Artificial intelligence (AI) has rapidly advanced in recent years, encompassing broad domains such as machine learning (ML) and natural language processing (NLP). NLP is a subfield of AI focused on enabling computers to understand, interpret, and process human language, both spoken and written. It has found applications in various healthcare settings, including emergency call centers, medical imaging, and hospital patient support systems. In rheumatological diseases, patient history and physical examination play a critical role in diagnosis and treatment. Objective(s): This study aims to predict the specific rheumatological disease a patient may have by leveraging ML and NLP techniques on physicians' notes, which include patient history, physical examination findings, and clinical follow-up records written during outpatient clinic visits. Method(s): A total of 4167 patients with rheumatological diagnoses were included in the study. Among the diagnoses, the conditions with the highest number of patients included rheumatoid arthritis, lupus, spondyloarthritis, Sjogren's syndrome, psoriatic arthritis, fibromyalgia, and other diseases. Physician notes were preprocessed using an unsupervised learning model, where the "nominal to string" transformation was applied, followed by text processing with the "string to word vector" method. Decision tree, random forest, logistic regression binary classifier, and Naive Bayes algorithms were implemented, and the results of the model with the best performance are presented. Result(s): The ML model that provided the best performance metrics was the binary logistic regression classifier. The model (average weights) achieved a receiver operating characteristic (ROC) area of 0.966, a sensitivity of 0.784, a specificity of 0.800, a recall of 0.784, and a precision-recall curve area of 0.829. Conclusion(s): AI technologies are expected to continue spreading across all areas of medicine, providing clinicians with significant time savings. In the near future, their integration into hospital systems is anticipated to enhance the quality of professional healthcare services. REFERENCES: NIL. Acknowledgements: I thank Yusuf Kerem Yucel for setting up the computational environment. Disclosure of Interests: None declared. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc. Copyright © 2025 © Author(s) (or their employer(s)) 2025. No commercial re-use. See rights and permissions. Published by Elsevier Inc.

Corrigendum to "Lifelong learning tendencies in primary healthcare workers: A descriptive design" [Nurse Educ. Today 147 (2025) 106608]...Boyraz S, Hacialiogu N. Nurse Education Today. 2025;147:N.PAG-N.PAG.

Sun, 01 Jun 2025 04:00:00 +0000

Nurse Education Today; 06/01/2025
(AN 184228153); ISSN: 02606917
CINAHL Complete

Perceptions of Professional Development Among Critical Care Nurses in Jordan.

Sun, 01 Jun 2025 04:00:00 +0000

Journal of Continuing Education in Nursing; 06/01/2025
(AN 185493951); ISSN: 00220124
CINAHL Complete

Learning Shifts of Clinicians Who Become Clinician‐Coaches: An Exploratory Qualitative Study of Emergency Physicians.

Sun, 01 Jun 2025 04:00:00 +0000

Emergency Medicine Australasia; 06/01/2025
(AN 186112729); ISSN: 17426731
CINAHL Complete

Exploring the link between Paradoxical leadership and nurses' career maturity: the mediating role of organizational learning.

Mon, 19 May 2025 04:00:00 +0000

BMC Nursing; 05/19/2025
(AN 185281323); ISSN: 14726955
CINAHL Complete

Use of machine learning to predict creativity among nurses: a multidisciplinary approach.

Thu, 15 May 2025 04:00:00 +0000

BMC Nursing; 05/15/2025
(AN 185184892); ISSN: 14726955
CINAHL Complete

Assessing the relationship between supervisor knowledge sharing and innovative behaviors among clinical nurses: the mediating role of organizational learning.

Mon, 12 May 2025 04:00:00 +0000

BMC Nursing; 05/12/2025
(AN 185099229); ISSN: 14726955
CINAHL Complete

Evaluation of a Learning Collaborative to Implement Postgraduate Nurse Practitioner (NP) and/or Physician Associate (PA) Residency or Fellowship Programs in a Community Health Center.

Wed, 07 May 2025 04:00:00 +0000

Journal of Primary Care & Community Health; 05/07/2025
(AN 185067295); ISSN: 21501319
CINAHL Complete

Development of a learning needs assessment tool for perioperative nurses: A methodological study.

Thu, 01 May 2025 04:00:00 +0000

Nurse Education Today; 05/01/2025
(AN 183548465); ISSN: 02606917
CINAHL Complete

The Coalition of National Health Education Organizations: Recent Advances to Strengthen the Health Education Profession.

Thu, 01 May 2025 04:00:00 +0000

Health Promotion Practice; 05/01/2025
(AN 184338365); ISSN: 15248399
CINAHL Complete

Building Health Policy Advocacy Expertise in Pediatric Nurse Practitioners Through Civic-Based Experiential Learning.

Thu, 01 May 2025 04:00:00 +0000

Nurse Educator; 05/01/2025
(AN 184703488); ISSN: 03633624
CINAHL Complete

Nursing Professional Development Practitioners Engage in Formal Rounding to Support New Graduate Nurses' Transition to Practice.

Thu, 01 May 2025 04:00:00 +0000

Journal for Nurses in Professional Development; 05/01/2025
(AN 184829578); ISSN: 21699798
CINAHL Complete

Long-term effects of flipped learning on nurses' self-efficacy and satisfaction in pressure injury management: A multi-center quasi-experimental study.

Thu, 01 May 2025 04:00:00 +0000

Nurse Education in Practice; 05/01/2025
(AN 185129101); ISSN: 14715953
CINAHL Complete

The impact of digital competence on pedagogical innovation among nurse educators: The moderating role of artificial intelligence readiness.

Thu, 01 May 2025 04:00:00 +0000

Nurse Education in Practice; 05/01/2025
(AN 185129100); ISSN: 14715953
CINAHL Complete

Corrigendum to "The effect of self-directed learning via a smartphone application and small-group teaching on nursing students' learning of pressure injury assessment: A randomized controlled study" [Nurse Educ. Pract. 85 (2025) 104336]...Aydogan S, Calis

Thu, 01 May 2025 04:00:00 +0000

Nurse Education in Practice; 05/01/2025
(AN 185129106); ISSN: 14715953
CINAHL Complete

Practice Nurse bite-sized learning Understanding stable angina.

Thu, 01 May 2025 04:00:00 +0000

Practice Nurse; 05/01/2025
(AN 185409236); ISSN: 09536612
CINAHL Complete

Rare and Undiagnosed Disease: A Learning Program for Nurses and Midwives.

Thu, 01 May 2025 04:00:00 +0000

Nursing Reports; 05/01/2025
(AN 185476469); ISSN: 2039439X
CINAHL Complete

Automatic for the People: What Has Machine Learning Analysis of Positron Emission Tomography Left for the Physician?

Thu, 10 Apr 2025 04:00:00 +0000

Journal of Clinical Oncology; 04/10/2025
(AN 184263732); ISSN: 0732183X
CINAHL Complete

Reply to: Automatic for the People: What Has Machine Learning Analysis of Positron Emission Tomography Left for the Physician?

Thu, 10 Apr 2025 04:00:00 +0000

Journal of Clinical Oncology; 04/10/2025
(AN 184263733); ISSN: 0732183X
CINAHL Complete

Impacts of an online asynchronous continuing professional development toolkit supporting primary care nurses to engage in shared decision-making: a single-group pre-post study.

Thu, 10 Apr 2025 04:00:00 +0000

BMC Nursing; 04/10/2025
(AN 184386642); ISSN: 14726955
CINAHL Complete

Bachelor Thesis in nursing: A qualitative study of the teaching-learning process from the experience of the students, academic tutors and nurses.