Using structural equation modeling to examine the relationship between Ghanaian teachers' emotional intelligence, job satisfaction, professional identity, and work engagement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it