Influence of Gender, Spiritual Involvement/Belief and Emotional Stability on Prosocial Behavior among Some Nigerian Drivers
Bibliographic record
Abstract
Past studies focus less on the relationships among emotional stability, spiritual involvement and belief and prosocial behavior, especially among Nigerian Drivers. Therefore, this study investigated the extent at which emotional stability (High and Low), spiritual involvement and belief (High and Low) and gender difference (Male and Female) influenced prosocial behavior among Drivers in Oyo State. A 2×2×2 ANOVA was adopted to study 200 (100 males and 100 female Drivers). Questionnaire was used to gather information and the ages of the respondents ranged between 20 and 56 years (mean = 36.38; SD = 9.722). Results of the 2x2x2 ANOVAemployed indicated that drivers who reported high spiritual involvement/belief were found to be high on prosocial behavior compared to those who are low [F (1,192) = 10.825, p < 0.01]. Also, emotional stability has a significant influence on prosocial behavior [F (1,192) = 4.431, p < 0.05]. However, gender had no significant effect on prosocial behavior among Drivers. There were no significant interaction effects between these variables and prosocial behaviour. It was recommended that in order to enhance prosocial behavior, the level of spiritual involvement and emotional stability among Drivers need to be increased. Besides, it was recommended that appropriate government agencies should be contacted in case of any emergency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".