Psychological Burnout in Early Childhood Teachers: Levels and Reasons
Bibliographic record
Abstract
The primary education plays an important role in the life of an individual as well as the nation as a whole. This study aims at investigating the level of psychological burnout in early childhood female teachers at public and private kindergarten schools. It also aims to prove whether these burnout levels differ according to the type of school, academic specialization, social status, and age. This study aims to identify the major causes of psychological burnouts from the teachers’ perspectives. The researchers used both qualitative and quantitative approaches. In addition, the study sample consisted of 96 female teachers from all the kindergarten schools in Al-Salt educational directorate. The researchers were applied Maslach quantitative scale. Consequently, 8 female private kindergarten school teachers were interviewed. The main results showed that moderate to high levels of psychological burnouts are present. Thus, this is accompanied with statistical differences according to the type of kindergarten school and social status. No statistical differences recorded regarding academic specialization and age. The results also showed that work pressure, low income, and the management relationship with the parents are some of the main causes of psychological burnouts. The researchers recommended conducting more studies in order to reduce the reasons leading to psychological burnouts among teachers in general.
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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 | 0.001 |
| 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) | 0.002 | 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".