The Relationship Between Career Decision-Making Self-Efficacy and Emotional Intelligence, Career Optimism, Locus of Control and Proactive Personality: A Meta-analysis Study
Bibliographic record
Abstract
Although there are studies on career decision-making self-efficacy and emotional intelligence, career optimism, locus of control, and proactive personality, no study addresses these four variables together. Therefore, this meta-analysis study examined the correlational findings between career decision-making self-efficacy and four different variables (emotional intelligence, career optimism, locus of control, and proactive personality). In this study, studies published between 1993-2022 examining the relationship between the variables determined from 10 scientific databases (Eric, JSTOR, Sage Journal, Google Academic, Scopus, Springer Ling, Taylor, and Francis ULAKBİM, Proquest, EBSCO) and career decision-making self-efficacy were used. As a result of the research, career decision-making self-efficacy and optimism (r = 0.46; 95% CI [0.33, 0.57]), locus of control (r = 0.36; 95% CI [0.02, 0.62]), proactive personality (r = 0.47; %) 95 CI [0.37, 0.57]) and emotional intelligence (r = 0.45; 95% CI [0.35, 0.54]) were found to be significantly correlated. These critical results point to promising aspects for researchers and practitioners working in career counseling.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.027 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".