Impacts of the COVID-19 pandemic on the mental health of frontline hospital-based nurses: rapid review and meta-analysis
Bibliographic record
Abstract
The COVID-19 pandemic focused the world’s attention on the gross relative health risks, inequities and injustices experienced by first responders, of whom front-line, hospital-based nurses may be the most vulnerable. A series of research syntheses prior to the pandemic estimated that such front-line nurses were at approximately two-fold greater risk of experiencing mental health challenges such as increased symptoms (and diagnoses) of anxiety, depression and PTSD than were nursing administrators or otherwise similar, people in the general population. Aiming to clarify how the pandemic impacted such mental health risks, we conducted a rapid review and meta-analysis of observational studies from the worldwide published and gray research literature. Twelve longitudinal studies that covered the pandemic’s pre-vaccination phase were included. The overall pooled increased relative risk of mental health challenges among frontline, hospital-based nurses from pre-pandemic to the pre-vaccination phase of the COVID-19 pandemic was 2.62 (95% CI 2.10, 3.27). But sensitivity and moderator analyses accounting for research design limitations better estimated two-fold increased risks (relative risks ranged from 1.69 to 2.00). Already at elevated risk of experiencing symptoms of anxiety, depression and PTSD and so, such increased symptoms and diagnoses among front-line, hospital-based nurses probably doubled during the early, pre-vaccination phase of the pandemic, perhaps quadrupling among women. Implications for nursing practice, health care policy, future pandemic preparedness as well as future research are discussed.
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 | 0.021 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.031 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".