Analysis of the Relationship among Career Education Program Participation and Satisfaction, Work Value, Career Goal Setting in Higher Education
Bibliographic record
Abstract
This study aimed to analyze the causal model of career education program participation, satisfaction, and career goal setting mediated by work values in higher education. Also, this study analyzed the differences between departments. In this study, data from the 2018 Graduate Occupational Mobility Survey (GOMS), the most recent data, was used. The Graduates Occupational Mobility Survey is the largest short-term panel survey of a representative sample of Korean Graduates. The GOMS is conducted annually and has its results compiled each year. Structural equation modeling and multiple SEM were used for this study. The results were as follows. First, career education program participation had a statistically significant positive effect on extrinsic and intrinsic work values and career goal setting. Second, career education program satisfaction had a statistically significant positive effect on intrinsic work values and career goal setting, except for extrinsic work value. In addition, only the intrinsic work values positively affected career goal setting. Third, the career education program satisfaction impacted intrinsic work values, and the intrinsic work value impact on career goal setting was the difference between department and majors. In conclusion, this study proposed the provision of career education programs considering the difference between departments and majors in higher education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".