A Study of Iranian EFL Teachers' Attributions, Job Satisfaction, and Stress at Work
Bibliographic record
Abstract
Job satisfaction is generally defined as combination of psychological, attitudinal, emotional, and environmental circumstances which make a person content with his/her profession. The present study probed English as a foreign language (EFL) teachers’ job satisfaction in relation to attitudinal and emotional factors. In particular, it investigated the role of Iranian EFL teachers’ attributions and stress at work in their job satisfaction. Moreover, the impact of attributions on stress at work was examined. To this end, 134 Iranian EFL teachers were selected and asked to complete of three questionnaires: English Language Teacher Attribution Scale (TAS), a combined Job Descriptive Index (JDI) and Job in General(JIB) Scale, and Stress at Work (SAT) Scale. The results of structural equation modeling (SEM) indicated that among teacher attributions concerning job satisfaction, teaching competency (TC) and teacher effort (TE)—both internal attributions—predicted job satisfaction positively and significantly, with TE having a greater influence. The SEM analysis also explored the association of teacher attributions of job satisfaction and their stress levels at work. It was revealed that among the four attributions, TC and TE negatively and significantly predicted stress at work. Furthermore, the findings demonstrated that teacher stress at work is negatively and significantly associated with job satisfaction. The conclusion of the study is that EFL teachers’ job satisfaction and stress at work is related to internal, controllable, and unstable attributions. La satisfaction au travail se définit généralement comme une combinaison de circonstances psychologiques, comportementales, émotionnelles et environnementales qui font en sorte qu’une personne est satisfaite de sa profession. La présente étude porte sur la satisfaction au travail d’enseignants d’anglais langue étrangère (ALE) telle qu’indiquée par des facteurs comportementaux et émotionnels. Plus particulièrement, elle s’est penchée sur le rôle que jouent les attributions et le stress au travail des enseignants iraniens d’ALE dans leur satisfaction au travail. L’impact des attributions sur le stress au travail a également été examiné. Nous avons sélectionné 134 enseignants iraniens d’ALE et leur avons demandé de compléter trois questionnaires, un portant sur une échelle d’attributions pour enseignants d’anglais, un portant à la fois sur un index de descriptions de postes et une échelle des postes en général, et un portant sur une échelle de stress au travail. Les résultats d’une modélisation par équation structurelle indiquent que parmi les attributions portant sur la satisfaction au travail, la compétence en enseignement et les efforts des enseignants—toutes les deux des attributions internes—prédisent de façon positive et significative la satisfaction au travail (les efforts des enseignants ayant plus d’influence). La modélisation par équation structurelle a également révélé le lien entre les attributions de satisfaction au travail et les niveaux de stress au travail. Des quatre attributions, la compétence en enseignement et les efforts des enseignants prédisaient de façon négative et significative le stress au travail. De plus, les résultats ont indiqué que le stress au travail des enseignants est lié de façon négative et significative à la satisfaction au travail. Nous concluons que la satisfaction et le stress au travail des enseignants en ALE sont liés à des attributions internes, contrôlables et instables.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".