Long-term impacts of COVID-19 on stress and depression among teachers: Differences by gender
Bibliographic record
Abstract
This article explores the impact of changes in teaching modalities resulting from the COVID-19 pandemic on the mental health of K-12 teachers, by gender, during the first year of the pandemic. Teachers from a random sample of K-12 schools in North Dakota and Minnesota were surveyed in April 2020, October 2020, and March/April 2021 about their current levels of stress and depression, as well as the frequency with which they experienced certain physical conditions. One-way analysis of variance and multiple regression were used to compare time points for each of the outcomes by gender. Female teachers were more likely to experience higher levels of stress than male teachers, while male teachers were more likely to experience higher levels of depression than female teachers, with spikes in stress and depression levels experienced by both males and females in Time 2. Additionally, physical symptoms were more likely to be experienced by female teachers, with Time 2 respondents overall reporting significantly higher proportions of physical symptoms than Time 1 or Time 3 respondents. Consistently experiencing heightened levels of stress and depression can lead to burnout for teachers. School districts need to monitor stress, especially among females, and depression, especially among males, to recognize the difference in experience for each gender in the teaching profession, as well as provide supports and resources to their teachers to help them in coping with these mental-health issues.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".