Exploring nursing student self-esteem change and its predictors: Cohort study and its methodological challenges
Jacinthe Dancot1, Nadia Dardenne1, Anne-Françoise Donneau1
1Department of Public Health, University of Liège, Liège, Belgium.
Enfermeria Clinica
|July 4, 2024
Summary
Nursing student self-esteem moderately increased during their bachelor's program. Key predictors of this change included gender, prior education, anxiety, and self-efficacy, highlighting areas for potential intervention to improve student outcomes.
Area of Science:
- Nursing Education Research
- Psychology of Learning
- Student Well-being Studies
Background:
- Previous research on nursing student self-esteem has yielded inconsistent results.
- Methodological challenges have hindered a clear understanding of self-esteem trajectories in this population.
- A need exists for studies employing robust theoretical frameworks and validated instruments.
Purpose of the Study:
- To describe the longitudinal changes in nursing student self-esteem.
- To identify predictors of self-esteem fluctuation throughout a bachelor's program.
- To inform interventions aimed at enhancing student self-esteem and retention.
Main Methods:
- A cohort study design was employed, tracking nursing students over a 4-year bachelor's program.
- Self-esteem was measured annually using the revised Self-liking/Self-competence scale.
- Generalized linear mixed modeling was utilized for data analysis.
Main Results:
- Nursing student self-esteem was moderate at baseline and showed a slight but statistically significant increase over time.
- Significant predictors of self-esteem change included gender, secondary school graduation level, state anxiety, intent to continue studies, and self-efficacy.
- These factors offer potential targets for supportive interventions.
Conclusions:
- A bidimensional model, assessed via scales like the Tafarodi & Swann's self-liking/self-competence scale, is suitable for evaluating nursing student self-esteem.
- Generalized linear mixed models are effective for analyzing longitudinal data in this context.
- Interventions focusing on identified predictors could enhance student achievement and retention.
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