School staff and teachers during the second year of COVID-19: Higher anxiety symptoms, higher psychological distress, and poorer mental health compared to the general population
Bibliographic record
Abstract
Background: The aim of this study was to: 1) assess mental health symptoms in Canadian school staff during the second year of the pandemic (Spring 2021) and compare these same outcomes to national representative samples, and 2: examine whether the number of hours of direct contact with students was a significant predictor of anxiety symptoms. Methods: Online data on anxiety symptoms, psychological distress, overall mental health, and demographic information was collected from 2,305 school staff in the greater Vancouver area between February 3 and June 18, 2021, as part of a seroprevalence study. Results: School staff reported significantly higher anxiety symptoms than a national representative survey in Spring 2021 and higher exposure contact time with students was significantly associated with anxiety symptoms, in addition to sex and age, but not level of education and ethnicity. School staff also reported poorer mental health and higher levels of psychological distress compared to pre-pandemic population measures. Limitations: Cross-sectional design, self-report measures. Conclusions: These results show that priorities to reduce mental health challenges are critical during a public health crisis, not only at the beginning, but also one year later. Ongoing proactive prevention and intervention strategies for school staff are warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".