Understanding language teacher wellbeing: An ESM study of daily stressors and uplifts
Bibliographic record
Abstract
This study focuses on understanding language teachers’ lived experiences of their stressors and positive uplifts from a holistic perspective covering their professional lives in school, their personal lives beyond, and the connection between the two. The aim was to explore the nature of teachers’ experiences of stress and how they spilled over from work into home domains. We also were keen to understand the dynamics of their experiences of stress and how their perception of daily stressors was related to their overall sense of wellbeing as well as their life and chronic stressors. The data were collected via a specially created app, which collected survey data and experience sampling method (ESM) data from language teachers across the globe. Teachers’ wellbeing was investigated using the PERMA Profiler (Butler & Kern, 2016), their personality using Goldberg’s (1992) Big Five measurement tool, and a questionnaire on chronic stressors and stressful life events. From a larger sample ( n = 47), a set of 6 case studies of teachers who scored highly for wellbeing and those who scored low on wellbeing was examined to explore in depth and across time, the relationships between overall wellbeing, chronic stressors and stressful life events, the experience of daily stressors, and perceptions of health. The findings point to the complexity of the relationships between stress, wellbeing, and health as well as the dynamism of stress and the relationships between stress experienced in the workplace and at home. The study has important implications for research in this area and reveals the merits of working with this innovative data collection tool.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".