Staying Insecure or Adapting? Career Adaptability and Insecurity on Self-Efficacy in Final-Year Students in University
Bibliographic record
Abstract
Purpose – This study aims to examine the effects of career adaptability and career insecurity on self-efficacy among final-year students in university, focusing on the mediating role of career self-management. Methodology – A correlational multifactor model was used with a sample of 416 students from the academic year 2022/2023 in South Sulawesi, Central Sulawesi, and West Sulawesi Provinces, Indonesia. Data were collected using the Career Self-Management Inventory, Career Decision Self-Efficacy (CDSE), Career-Related Adaptability and Optimism (CFI), and Career Insecurity Scale. Data were analyzed using path analysis with the JASP application. Findings – The findings revealed that career insecurity does not directly or indirectly affect self-efficacy, while career adaptability and optimism directly influence self-efficacy. Career insecurity showed no effect on adaptability, whereas optimism indirectly increased self-efficacy through career self-management. The career self-efficacy of final-year students is influenced by internal individual factors such as adaptability and career self-management, while career insecurity is an external factor that, although it affects students' feelings, does not diminish their confidence in achieving career success in the future. Novelty – The study uniquely explores the relationship between career insecurity, career adaptability, and self-efficacy during the career transition period among final-year students at the University. Significance – The findings can aid universities in developing curricula that not only prepare students in their fields of study but also enhance student’s career adaptability and self-management to the working world.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".