Stress, Coping, and Well-being in Teachers and School Administrators
Bibliographic record
Abstract
This study explored educators’ self-reported state of well-being, perceived stressors, and use of coping strategies. Data collection consisted of an online survey and semi-structured focus groups. In total, 115 educators completed the online survey and 18 educators participated in the focus groups. Educators reported overall experiences of poor well-being, low resilience, high levels of compassion fatigue, and high levels of emotional exhaustion. Participants identified ongoing stressors related to supporting student learning and well-being, overseeing classroom environments, navigating limited community-based resources and supports, and managing increasing administrative demands and functions. Implications of the findings for practice are discussed. Keywords: Stress; Coping; Well-being; Educators; Mental Health Cette étude a exploré l'état de bienêtre déclaré par les éducateurs, les facteurs de stress perçus et l'utilisation de stratégies d'adaptation. La collecte des données a consisté en une enquête en ligne et des groupes de discussion semi-structurés. Au total, 115 éducateurs ont répondu à l'enquête en ligne et 18 éducateurs ont participé aux groupes de discussion. Les éducateurs ont fait état d'expériences globales de malêtre, de faible résilience, de niveaux élevés d'usure de la compassion et de niveaux élevés d'épuisement émotionnel. Les participants ont identifié des facteurs de stress permanents liés au soutien de l'apprentissage et du bienêtre des élèves, à la supervision des environnements de classe, à la navigation dans les ressources et soutiens communautaires limités, et à la gestion des demandes et fonctions administratives croissantes. On discute des implications des résultats pour la pratique. Mots clés : stress ; adaptation ; bienêtre ; éducateurs ; santé mentale
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".