The Relationship Between Emotional Intelligence and Career Decision-Making Difficulties: Mediation Role of Career Adaptability of University Students
Bibliographic record
Abstract
Rapid changes in the global marketplace and invisible pressures have made career decision-making challenging for students. This article explores the relationship between emotional intelligence and career decision difficulties, as well as the mediating role of career adaptability, in a cross-sectional study conducted among students. A total of 265 students from Ho Chi Minh City, Vietnam (Mage = 19.99; SD = 1.46) were randomly selected and participated through an online questionnaire. The study employed the Career Decision-making Difficulties Questionnaire, which includes 34 questions to assess career decision-making difficulties; the Wong and Law Emotional Intelligence Scale, comprising 16 items to measure emotional intelligence; and the Career Adapt-Abilities Scale - Short Form with 12 items to evaluate career adaptability. The findings reveal two primary outcomes: (1) emotional intelligence significantly negatively impacts career decision difficulties (effect = -0.15, p < .05, 95% CI = [-0.29, -0.01]), and (2) career adaptability significantly mediates this relationship (effect = -0.11, 95% CI = [-0.20, -0.02]). These results suggest that enhancing emotional intelligence and career adaptability may facilitate improved career decision-making among students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".