Factors Related to Teacher Resilience during COVID-19
Bibliographic record
Abstract
Teaching during the 2020–2021 school year was fraught with challenges related to the COVID-19 pandemic. In the United States, teacher experiences varied greatly. Teacher attrition has been a concern for years, and contemporary media outlets reported that this was exacerbated by the pandemic. The authors surveyed teachers nationally between January and February 2021 (n = 334) to uncover what factors were related to teachers’ reported intention to remain in the classroom after the 2020– 2021 school year. Logistic regression findings indicate that teachers approaching retirement age and those teaching in private schools were significantly less likely to report an intention to remain at their school while elementary school teachers were more likely to stay. Conversely, we found that teacher autonomy, job satisfaction, and student access to resources outside of school were all positively associated with an intention to remain in their current position. RésuméAu cours de l’année scolaire 2020–2021, l’enseignement a fait face à de nombreux défis reliés à la pandémie de la COVID-19. Aux États-Unis, les expériences des enseignants ont été très diverses. Depuis des années, l’érosion de l’effectif est un souci, et les médias contemporains signalent que la pandémie a augmenté celle-ci. En janvier et février 2021, les auteurs ont sondé des enseignants à l’échelle nationale (n = 334) afin de relever les facteurs ayant motivé ceux-ci à vouloir continuer audelà de 2020–2021. Une régression logistique effectuée par les auteurs indique que les enseignants proches de la retraite et ceux travaillant dans des écoles privées étaient moins enclins à rapporter l’intention de rester dans leurs écoles tandis que les enseignants des écoles élémentaires avaient davantage l’intention de persévérer. En général, les auteurs ont trouvé que l’autonomie de l’enseignant, la satisfaction au travail, et l’accès des étudiants à des ressources au-delà de leur école étaient tous positivement associés au désir de continuer à enseigner. Keywords / Mots clés : COVID-19, teacher retention, teacher attrition, teacher autonomy, job satisfaction, retirement age / COVID-19, fidélisation des enseignants, attrition des enseignants, autonomie des enseignants, satisfaction au travail, âge de la retraite
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".