Exploring the relationship between meaning at work and subjective wellbeing among language teachers
Bibliographic record
Abstract
The concept of meaning at work has gained significant attention in organizational psychology. As teaching is a profession often associated with high levels of emotional investment and personal engagement, understanding how the perception of meaning in work influences teachers’ wellbeing is crucial. This study aimed to explore the relationship between meaning at work and subjective wellbeing among English as a Foreign Language (EFL) teachers. It sought to quantify this relationship through regression analysis and further elucidate it through thematic analysis of qualitative interview data. The study involved 65 EFL teachers, with data collected via the Work and Meaning Inventory (WAMI) and the Subjective Well-being Scale. Linear regression analysis was used to examine the relationship between meaning at work and subjective wellbeing. Additionally, in-depth interviews with 12 teachers provided qualitative insights, analyzed through thematic analysis to identify emergent themes relating to meaning at work and its impact on wellbeing. Regression analysis revealed a significant positive relationship between meaning at work and subjective wellbeing (R² = .394, p < .001). The thematic analysis of interview data yielded four main themes: personal fulfillment and job satisfaction, professional growth and self-realization, sense of purpose and contribution to society, and connection and relationships. These themes underscored the multifaceted ways in which meaning at work contributes to language teachers’ wellbeing. The study highlights the profound impact of meaning at work on EFL teachers' subjective wellbeing. The findings emphasize the importance of fostering meaningful work environments in educational settings to enhance teacher wellbeing, with implications for teacher retention, job satisfaction, and overall school climate.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".