Using structural equation modeling to examine the relationship between Ghanaian teachers' emotional intelligence, job satisfaction, professional identity, and work engagement
Bibliographic record
Abstract
Abstract The purpose of the study was to examine the causal relationship between teachers' emotional intelligence, job satisfaction, professional identity, and work engagement. And to achieve this purpose, a questionnaire consisting of four scales was administered to 260 teachers selected from the Adentan Municipal in the Greater Accra Region. Exploratory factor analysis, structural equation modeling, and univariate statistical analyses were employed to analyze the data. Results of the analyses established that job satisfaction mediated the relationship between teachers' emotional intelligence and work engagement. The findings also revealed that emotional intelligence positively affected professional identity directly and indirectly through job satisfaction. It was further revealed that female teachers exhibited more professional identity and were more satisfied than their male counterparts. The study concluded with the recommendation that for Ghanaian teachers to be actively engaged with their job, they should be provided with the opportunity to develop and improve their emotional intelligence. It was also recommended that a module on emotional intelligence be included in the curriculum for training pre‐service teachers.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".