Effects of Career Decision-Making Self-Efficacy, Career Outcome Expectation, and Career Consciousness Maturity on Career Preparation Behavior of Nursing Students
Bibliographic record
Abstract
Although career preparation is an important factor in making correct career decisions and increasing job satisfaction, nursing students lack consideration and preparation for their career path when choosing a major. The purpose of this study was to identify the relationships among career decision-making self-efficacy, career outcome expectation, and career consciousness maturity on the behavior of nursing students in preparing for their careers. We collected data using structured questionnaires from 95 nursing students in C city from June 1 to June 20, 2018. We analyzed the data using the IBM SPSS/WIN 23.0 program for descriptive statistics, independent t test, oneway ANOVA, Pearson’s correlation coefficient, and multiple regression. As a result, the factors influencing the career preparation behavior of nursing students were career decision self-efficacy (β = .35, p < .001) and career consciousness maturity (β = .30, p = .003), and the explanatory power of these variables was 37%. Based on these results, it was required to develop programs to strengthen career decision-making self-efficacy and career consciousness maturity. In order to improve career decision-making self-efficacy, a program should be developed to improve confidence in solving problems by providing career opportunities. In addition, in order to strengthen career consciousness maturity, a career road map for each grade should be constructed, as well as systematic career counseling and employment capacity enhancement programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".