Examining the contributions of hope and optimism to teacher wellbeing and burnout through a structural equation modeling analysis
Bibliographic record
Abstract
This study investigated the contributions of hope and optimism to teacher well-being and burnout through a Structural Equation Modeling (SEM) framework. A sample of 200 Iranian language teachers completed validated measures of hope, optimism, well-being, and burnout. Correlation analyses revealed strong positive relationships between hope and optimism, as well as their significant positive associations with well-being and negative associations with burnout. Mediation analyses demonstrated that burnout partially mediated the effects of hope and optimism on well-being. Path analysis indicated that optimism had a stronger protective influence against burnout ( β = -0.48) and a more pronounced effect on well-being ( β = 0.34) compared to hope ( β = -0.37 and β = 0.26, respectively). The findings highlight the distinct and complementary roles of hope and optimism in enhancing teacher well-being and reducing burnout, underscoring the importance of fostering these psychological resources to promote resilience and sustainable professional practice. By adopting a SEM approach, this study provides a more nuanced understanding of the pathways through which hope and optimism interact with burnout and well-being, offering insights for intervention and teacher support programs.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".