Inside UK Universities: Staff mental health and wellbeing during the coronavirus pandemic
Bibliographic record
Abstract
This report documents the mental health and wellbeing of university staff during the coronavirus pandemic, using survey data collected online in March 2021 from 1,182 staff employed across 92 UK universities. Overall, the survey data suggest that university staff are grappling with high levels of poor mental health and wellbeing:• One in two university staff reported experiencing chronic emotional exhaustion (55%), worry (53%), and stress (51%) during the academic year 2020/21.• Half of the staff surveyed (47%) described their mental health as poor.• Over a third of staff members reported low life satisfaction (36%).• More than a quarter of staff reported feeling as if the things they did in their lives were not worthwhile (27%).• One in two staff members experienced high levels of anxiety (50%) – 1.5 times higher than the national average (32%).• One in three university staff reported low levels of happiness (33%) compared with a national average1 of one in seven (14%).In this report, we explore factors that may alleviate the burden of poor mental health and wellbeing amongst HE staff. Factors that fall more within the remit of institutions include social inclusion and the alignment between skills and task demands. Factors that fall more within the remit of government and policy makers include autonomy and the value that is placed on universities and their staff. In publishing this report, we hope institutional leaders and policy makers will recognise the urgent need to improve staff mental health and wellbeing. As we approach another academic year impacted by Covid-19 and universities in England brace themselves for funding cuts in the next spending review, action is needed to prevent a further deterioration in staff mental health and wellbeing.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".