Influence of Meaning in Life, Resilience, and Personality Traits on Depression among Undergraduates in the University of Ibadan, Oyo State, Nigeria
Bibliographic record
Abstract
The Meaning in life questionnaire, Resilience scale (RS-14), Personality scale (10 items), and the Depression scale were used to assess the influence of meaning in life, resilience, and personality traits on depression among undergraduates in the University of Ibadan, Oyo State, Nigeria. The sample consisted of 500 students, 351 male and 149 female students were selected across different departments. The study adopted quantitative research design, and accidental sampling technique was used to collect data. Six hypotheses were formulated and tested at .05 level of significance. The result revealed a significant joint influence of the predictors on depression. Further, resilience and emotional stability independently predicted depression among the participants. Moreover, it was revealed that search for meaning in life did not significantly influence depression. The study also revealed that gender significantly influenced depression. The study concluded that meaning in life, resilience and personality traits had influence on depression among undergraduates in the University of Ibadan, Oyo state, Nigeria, and it was recommended that the University management should incorporate resilience skills program to strengthen undergraduates against academic challenges and adversity as means for intervention & prevention against depression.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".