Influence of Supervisory Behaviour and Job Stress on Job Satisfaction and Turnover Intention of Police Personnel in Ekiti State
Bibliographic record
Abstract
This study examined the influence that supervisory behaviour and job-induced stress might have on job satisfaction and turnover intention of police personnel in Ekiti State, Nigeria. Three hundred and fifty police personnel (200 males, & 150 females) drawn from the three senatorial districts of Ekiti State responded to Supervisory Behaviour Description Questionnaire (SBDQ), Job Related Tension Questionnaire (JRTQ), Minnesota Satisfaction Questionnaire (MSQ) and Turnover Intention Question (TIQ). Analysis of data with 2x2 ANOVA statistics revealed a significant effect of supervisory behaviour on job satisfaction (F (1/346) = 185.4, P<0.1), a significant influence of supervisory behaivour on turnover intention (F (1/346) = 129.66, P<0.1) and a significant effect of job stress on Job satisfaction (F (1/346) =5.57, P<0.1). Further revealed is a significant effect of job stress on turnover intention (F (1/346) = 4.90, P<.0.1). However no significant interaction effect of supervisory behaviour and job stress was observed on Job satisfaction (F (1/346) = 2.16, P>. 0.5). In like manner there was no significant interaction effect of supervisory behaviour and job stress on turnover intention (F(1/346) = 3.37, P>.0.5). Findings were discussed in light of literature on supervisory behaviour, job stress, job satisfaction turnover intention and the police.
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 | 0.000 | 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".