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Influence of Gender, Spiritual Involvement/Belief and Emotional Stability on Prosocial Behavior among Some Nigerian Drivers

2014· article· en· W1916961439 on OpenAlexvenueno aff
Olukayode Ayooluwa Afolabi, Emmanuel Oyetunji Idowu

Bibliographic record

VenueCanadian social science · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsProsocial behaviorPsychologyDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.007
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.312
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2014
Admission routes1
Has abstractyes

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