Purpose: This study describes how an employer-based tuition-assistance program for homecare workers at one Canadian homecare organization enabled nursing career advancement and retention.
Design: A convergent parallel mixed-methods design.
Methods: We reviewed existing administrative data and concurrently conducted semi-structured interviews. Descriptive statistics were used on quantitative data and qualitative data was analyzed using thematic analysis. A joint data display was developed to integrate findings from both quantitative and qualitative data together.
Findings: Tuition assistance reduced financial barriers to career advancement; 83% of recipients remained with their employer for at least 1-year post-studies but only 29% experienced career advancement. Psychosocial supports, career navigation and coaching to ease the licensing and role transition processes were identified as opportunities to support learners.
Conclusion: Employer-based tuition assistance programs are impactful in helping to develop skilled employees. Practical enhancements to further support career transitions may maximize retention to address urgent homecare staffing challenges.
Clinical Evidence: Employer-based tuition assistance can be a useful strategy to support nursing career growth and staff retention.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1080/07370016.2024.2314077 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A Graduate Medical Education Fellowship in Climate Change and Human Health: Experience and Outcomes From the First 5 Years.
J Grad Med Educ
December 2024
is Director, Fellowship in Climate Change and Human Health, Assistant Professor, Harvard Medical School, and Assistant Professor, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Climate change is affecting health and health care, but most physicians lack formal training on climate change. There is a need for graduate medical education (GME) programs that prepare physician leaders to address its health impacts. To describe the development and iterative piloting of a GME fellowship in climate change and health and to assess fellows' academic output and public engagement before and after fellowship matriculation.
View Article and Find Full Text PDFDual assurance for healthcare and future education development: normalized assistance for low-income population in rural areas-evidence from the population identification.
Front Public Health
December 2024
Institute of Urban Development and Strategy, Law School, Research Center for Digitalization and Rural Development, Hangzhou City University, Hangzhou, China.
Introduction: This study aims to explore the relationship between healthcare and future education among the rural low-income population, using City in Guangdong Province as the focal area. Addressing both healthcare and educational concerns, this research seeks to provide insights that can guide policy and support for this demographic.
Methods: Utilizing big data analysis and deep learning algorithms, a targeted intelligent identification classification model was developed to accurately detect and classify rural low-income individuals.
Education cost as a new fickle in higher education for students learning via quantitively multinomial logistic regression.
Sci Rep
December 2024
Department of Medicinal Plants, Faculty of Agriculture and Natural Resources, Arak University, Arak, 38156-8-8349, Arak, Iran.
Article Synopsis
- The study investigated how tuition fees affect the quality of education for students at the Islamia University of Bahawalpur in Pakistan, examining factors that influence students' ability to pay.
- Data was collected through an online survey, revealing that high tuition costs negatively impact student performance, confidence, and career options, whereas students from affluent backgrounds or those working part-time tend to perform better.
- The research emphasizes the need for government action to improve student outcomes, such as subsidizing tuition and providing scholarships, and contributes valuable insight into the topic of tuition fees in Pakistan for future researchers.
Artificial intelligence improves risk prediction in cardiovascular disease.
Geroscience
November 2024
School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
Cardiovascular disease (CVD) represents a major public health issue, claiming numerous lives. This study aimed to demonstrate the advantages of employing artificial intelligence (AI) models to improve the prediction of CVD risk using a large cohort of relatively healthy adults aged 70 years or more. In this study, deep learning (DL) models provide enhanced predictions (DeepSurv: C-index = 0.
View Article and Find Full Text PDFBackground: Implementation science increasingly aims to improve equity in delivery of evidence-based interventions. It is important to expand the conceptualization of the inner setting, organizations like community health centers where interventions are put into place, accordingly. Taking a comprehensive, partnered approach to measuring the inner setting among a network of community health centers engaged in implementation research ensures assessment of the variability among sites and generates centralized contextual data that can be applied across studies.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!