Career Decision Self-efficacy Mediates the Relationship Between General Self-efficacy and Career Decision-making of Undergraduate Students
Bibliographic record
Abstract
Career decision-making has remained a constant challenge for students due to a lack of appropriate policy aimed at assisting students in making informed career decisions. This paper examined whether general self-efficacy of undergraduate students would predict their career decision-making through career decision self-efficacy. We obtained data from a convenient sample of 453 undergraduate students in a Ghanaian public university in a cross-sectional survey. The sample comprised 261 (57.6%) males and 192 (42.4%) females, with ages ranging from 18 to 33, and an average of 21.32 (SD = 2.25) years. Data were analysed using Hayes PROCESS for mediation (model 4). General self-efficacy significantly predicted both career decision self-efficacy and career decision-making. Also, career decision self-efficacy was a better predictor of students’ career decision-making than general self-efficacy was. Furthermore, career decision self-efficacy significantly mediated the relationship between general self-efficacy, and career decision-making. We concluded that even though both general self-efficacy and career decision self-efficacy were relevant predictors of career decision-making, context specific self-efficacy is more relevant than general self-efficacy. Implications of the findings for interventions and policy have been discussed within the social cognitive career and the self-determination theories.
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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 | 0.000 |
| 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) | 0.005 | 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".