Investigating the Role of Emotional Intelligence and Role Conflict on Job Burnout among Special Education Teachers
Bibliographic record
Abstract
The need to take appropriate care of children with disabilities by the relevant stakeholders as the world moves into the post-COVID era has become imperative. Hence, this work investigated how family-to-work, work-to-family conflicts, and emotional intelligence influenced the four dimensions of job burnout (enthusiasm towards the job, psychological exhaustion, indolence, and guilt) among teachers administering children with disabilities. This was assessed using a cross-sectional online survey design of 276 special education teachers (female = 159; mean age = 32.5, SD = 10.1) from the Nigerian population of teachers. Data were collected using structured psychological tests, including the Work and Family Conflict Scale (WFCS), Emotional Intelligence Scale, and Job Burnout Scale. Results indicated that emotional intelligence predicted all the dimensions of job burnout in teachers except psychological exhaustion. The independent variables failed to predict psychological exhaustion; however, family-to-work conflict independently predicted indolence. Furthermore, the results revealed no gender difference in all four dimensions of job burnout. Based on these findings, it was recommended that an intervention strategy targeting the promotion of emotional intelligence and adequate provision of modern facilities to be used to assist teachers in their special skills delivery.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".