Problem-Solving Approach Training on Professional Readiness Perception
NCT ID: NCT06828029
Last Updated: 2025-03-26
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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ENROLLING_BY_INVITATION
NA
130 participants
INTERVENTIONAL
2025-03-05
2025-06-01
Brief Summary
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The hypothesis of the study is that problem-solving approach training will enhance the professional readiness perception of newly hired nurses. It is anticipated that the training will improve the nurses' professional knowledge and skills, equipping them to better handle the clinical challenges they will encounter.
The study is planned to take place between February and June 2025 at Zoom platform. The population of the study consists of newly hired nurses, and a sample size calculation has been performed using power analysis. Effect size (Cohen's d): An effect size of 0.5 (moderate effect) has been assumed for this study. Alpha level (α): A Type I error rate of 0.05 is used. Power (1 - β): A power of 0.90 (90%) has been targeted.
Using these parameters and the G\*Power software, the sample size is calculated to be approximately 64 participants per group. Therefore, the target is to recruit at least 128 participants for the study.
Participants will be randomly assigned to experimental and control groups. The experimental group will receive online and interactive problem-solving approach training. Participants in the experimental group will be supported with problem-solving-based training modules, focusing on decision-making skills related to nursing practice throughout the training process. The control group will not receive any training. After the training, both groups' professional readiness perceptions will be assessed using the previously determined Personal Information Form and Professional Readiness Scale.
The data collected in the study will be analyzed using SPSS (Statistical Package for Social Sciences) for Windows 22.0 software. Kolmogorov-Smirnov test will be used to determine whether the data follows a normal distribution. If p \> 0.05, the data will be considered to follow a normal distribution. If p \< 0.05, the data will be considered not normally distributed, and skewness and kurtosis values will be examined. For repeated measures, if the data is normally distributed, repeated measures ANOVA will be used; if the data is not normally distributed, Friedman's test will be applied. Descriptive statistics will first be used to summarize the general characteristics of the data, and the means and distributions of pre-test and post-test scores will be determined. To evaluate the effect of the training intervention, paired samples t-test will be applied to assess the differences between pre-test and post-test scores within the same group. This analysis will be used to measure the effect of the training program on time management and procrastination behaviors. Additionally, effect size (e.g., Cohen's d) will be calculated to evaluate the magnitude and significance of the training intervention's effect. Analysis of variance (ANOVA) will be used to determine whether there are significant differences between the groups, depending on the study's needs.
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Detailed Description
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Conditions
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Study Design
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RANDOMIZED
PARALLEL
HEALTH_SERVICES_RESEARCH
SINGLE
Study Groups
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the experimental group who started working at the hospital
Problem solving training was applied to the experimental group.
Problem-Solving
The problem-solving training consists of five modules, blending theory and practice. Module 1 covers the basics of problem-solving, including key steps like identification, analysis, and evaluation, with group activities for hands-on practice. Module 2 focuses on creative techniques, such as brainstorming and mind mapping, to develop innovative solutions. Module 3 teaches decision-making and prioritization, using tools like SWOT analysis and decision trees, with applied exercises. Module 4 emphasizes teamwork and communication, highlighting conflict resolution and effective collaboration through team-based problem-solving activities. Module 5 integrates all techniques, applying them to real-life scenarios, particularly clinical cases, with group discussions and solution evaluations. The training incorporates interactive learning, simulations, and structured feedback, aiming to enhance problem-solving and leadership skills. Evaluation includes self-assessment and performance observation
control grubu
No training was provided.
No interventions assigned to this group
Interventions
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Problem-Solving
The problem-solving training consists of five modules, blending theory and practice. Module 1 covers the basics of problem-solving, including key steps like identification, analysis, and evaluation, with group activities for hands-on practice. Module 2 focuses on creative techniques, such as brainstorming and mind mapping, to develop innovative solutions. Module 3 teaches decision-making and prioritization, using tools like SWOT analysis and decision trees, with applied exercises. Module 4 emphasizes teamwork and communication, highlighting conflict resolution and effective collaboration through team-based problem-solving activities. Module 5 integrates all techniques, applying them to real-life scenarios, particularly clinical cases, with group discussions and solution evaluations. The training incorporates interactive learning, simulations, and structured feedback, aiming to enhance problem-solving and leadership skills. Evaluation includes self-assessment and performance observation
Eligibility Criteria
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Inclusion Criteria
* Working for a maximum of 6 months
* Graduated from the Department of Nursing
* To know and understand Turkish at an adequate level
* Voluntary participation
* Access to online education platforms
* Participated in at least 4 out of 5 weekly trainings
Exclusion Criteria
* The student requests to leave the study
* Continuing formal education
* Not working at the hospital
18 Weeks
ALL
Yes
Sponsors
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University of Beykent
OTHER
Responsible Party
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Seda Tugba Baykara Mat
Asst. Prof.
Locations
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Istanbul Beykent University
Istanbul, Istanbul, Turkey (Türkiye)
Countries
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Other Identifiers
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The Effect of Problem-Solving
Identifier Type: -
Identifier Source: org_study_id
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