Effect of Cognitive-Behavioural Group Guidance on Entrepreneurial Intention Among University Sandwich Education Students
Bibliographic record
Abstract
OBJECTIVE: The study objective was to determine the effects of cognitive-behavioural group guidance on entrepreneurial intention among university sandwich education students. MATERIALS & METHOD: The design of the study was a group randomized trial involving pretest and posttest, while the area of the study was a federal university in South-East Nigeria. Entrepreneurial Intention Questionnaire (EIQ) was used for data collection while the data collected were analysed through analysis of covariance with repeated measures. RESULTS: The results indicated that there was no significant difference between the participants’ entrepreneurial intention in the treatment and no-treatment control groups at the initial measure; and that after cognitive-behavioural group guidance intervention, there was a significant increase in entrepreneurial intention among the participants in the treatment group comparing to their counterparts in the no-treatment control group. CONCLUSION: Cognitive-behavioural group guidance was effective in increasing entrepreneurial intention among university sandwich education students. It was therefore concluded that counsellors should adopt the techniques used in the study to help individuals increase their entrepreneurial intention, and that cognitive-behaviour group guidance should be adopted as counselling approach for helping university students develop intentions to venture into entrepreneurial business.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".