“I feel like I lost myself”: An Examination of Teachers’ Lived Experiences During The COVID-19 Pandemic
Bibliographic record
Abstract
The current study aimed to understand teachers' lived experiences during the COVID-19 pandemic, revealing six main themes from their interviews and open-response questions. Key findings of teachers' lived experiences included the prevalent feeling of isolation due to a lack of social connection with students and colleagues, the struggle to balance various responsibilities, the increased workload transferring an interactive in-person environment to online learning and adhering to safety protocols. Despite these challenges, some teachers reported benefits such as improved work-life balance and enhanced technology skills. The findings also highlight differences between elementary and secondary school teachers, particularly in implementing safety measures, and how these varied based on years of teaching experience. Elementary school teachers faced unique challenges in maintaining young students' engagement and adherence to safety measures, whereas secondary school teachers experienced challenges related to subject-specific teaching demands. Additionally, teachers have demonstrated resilience and dedication, adapted their roles as advocators, educators, and support systems to ensure educational success throughout the various stages of the COVID-19 pandemic. This study fills a gap in existing research by specifically examining the distinct challenges and benefits experienced by teachers during this unprecedented period. By providing nuanced insights into teachers' experiences, this research contributes to understanding the broader impacts of the pandemic on educational practices and teacher well-being.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".