Organizational Commitment and Job Satisfaction as Determinant of Primary School Teachers Turnover Intention
Bibliographic record
Abstract
This study examines the predictive power of organizational commitment and job satisfaction on primary school teacher’s turnover intention. Two hundred primary school teachers participants were selected from 20 primary schools out of one hundred primary school in ijebu north local government area of Ogun State through stratified and simple random sampling techniques the school were selected from two (2) constituencies in ijebu north local government area which are Ifelodun and Ijebu-Igbo. Seven (7) schools were selected from ifelodun and thirteen (13) public primary schools were randomly selected from ijebu. Ten (10) teachers (male and female) were randomly selected from each school making a total of two hundred (200). The selection of the schools was based on the population and the geographical territory of the consistency. Three instruments, Turnover Intention Scale, organization commitment scale and intrinsic motivation inventory were utilized in the study. Multiple regression (stepwise) and simple percentage were used for analysis. Findings showed that the two determinant variables (organizational commitment and intrinsic motivation) when taken together, determined the criterion variable (turnover intention). Finding also indicated that organization commitment was the most potent contributor to the prediction of turnover intention of primary school teachers. The implications of these findings for the government, policy makers and employers of labor, who are interested in effective functioning and retention of workers, were discussed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".