Post-Traumatic Growth and Protection From Burnout in Teachers During the COVID-19 Pandemic
Bibliographic record
Abstract
Teachers have demonstrated a wide range of responses to the challenges of teaching within a pandemic. The current study investigated the relationship between resilience and post-traumatic growth in teachers during the COVID-19 pandemic. Based on administration of the Maslach Burnout Inventory in 2022, four teachers demonstrated high levels of resilience, indicating the potential that they had experienced post-traumatic growth. Follow-up interviews were therefore conducted to investigate whether these teachers demonstrated all seven characteristics of post-traumatic growth. Although all four resilient teachers indicated experiencing several characteristics, only two teachers demonstrated post-traumatic growth. The findings support a clear distinction between resilience and post-traumatic growth. Implications related to future research using both qualitative and quantitative methods to further illuminate the processes and conditions of post-traumatic growth are discussed. Keywords: Post-traumatic growth, teachers, pandemic, resilience, Canada Les enseignants ont fait preuve d'un large éventail de réactions face aux défis de l'enseignement dans le cadre d'une pandémie. La présente étude a examiné la relation entre la résilience et la croissance post-traumatique chez les enseignants pendant la pandémie de COVID-19. L'administration du Maslach Burnout Inventory, un questionnaire évaluant les symptômes de l’épuisement professionnel, en 2022 a révélé des niveaux élevés de résilience chez quatre enseignants, indiquant la possibilité qu'ils aient connu une croissance post-traumatique. Des entrevues de suivi ont donc été menés pour déterminer si ces enseignants présentaient les sept caractéristiques de la croissance post-traumatique. Bien que les quatre enseignants résilients aient indiqué avoir vécu plusieurs de ces éléments, seuls deux enseignants ont fait preuve d'une croissance post-traumatique. Les résultats soutiennent une distinction claire entre la résilience et la croissance post-traumatique. L’article discute des implications liées aux recherches futures utilisant des méthodes qualitatives et quantitatives pour éclairer davantage les processus et les conditions de la croissance post-traumatique. Mots clés : croissance post-traumatique, enseignants, pandémie, résilience, Canada
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".