Career Readiness Trends Among Final-Year Undergraduates in a Public University in Ibadan, Nigeria
Bibliographic record
Abstract
The transition from university to the professional world marks a crucial stage, and being well-prepared for one's career is essential for success. However, existing literature indicates a gap between skills obtained through tertiary education and the realities of the world of work. Hence, this research explored the potential differences in how final-year undergraduates in Ibadan perceive their preparedness for careers, examining the impact of age and gender on career readiness skills. Employing a descriptive cross-sectional design, the study involved a sample of 320 final-year students from a public University in Ibadan, randomly selected to respond to a 35-item questionnaire. The results revealed that male students exhibited a higher degree of career readiness compared to their female counterparts. Furthermore, the study showed that age did not significantly affect career readiness. Conversely, students rated themselves highly in teamwork, professionalism, and critical thinking skills, moderately in leadership, digital technology, and communication skills, while scoring lower in global and cultural fluency as well as career management skills. These findings underscore the need for enhanced career programs and guidance within tertiary institutions across Nigeria. Moreover, the study can offer valuable insights to policymakers for crafting strategies aimed at bolstering students' readiness for their future careers.
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 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".