Nightmare frequency is a risk factor for suicidal ideation during the <scp>COVID</scp>‐19 pandemic
Bibliographic record
Abstract
The association between nightmare frequency (NMF) and suicidal ideation (SI) is well known, yet the impact of the COVID-19 pandemic on this relation is inconsistent. This study aimed to investigate changes in NMF, SI, and their association during the COVID-19 pandemic. Data were collected in 16 countries using a harmonised questionnaire. The sample included 9328 individuals (4848 women; age M[SD] = 46.85 [17.75] years), and 17.60% reported previous COVID-19. Overall, SI was significantly 2% lower during the pandemic vs. before, and this was consistent across genders and ages. Most countries/regions demonstrated decreases in SI during this pandemic, with Austria (-9.57%), Sweden (-6.18%), and Bulgaria (-5.14%) exhibiting significant declines in SI, but Italy (1.45%) and Portugal (2.45%) demonstrated non-significant increases. Suicidal ideation was more common in participants with long-COVID (21.10%) vs. short-COVID (12.40%), though SI did not vary by COVID-19 history. Nightmare frequency increased by 4.50% during the pandemic and was significantly higher in those with previous COVID-19 (14.50% vs. 10.70%), during infection (23.00% vs. 8.10%), and in those with long-COVID (18.00% vs. 8.50%). The relation between NMF and SI was not significantly stronger during the pandemic than prior (rs = 0.18 vs. 0.14; z = 2.80). Frequent nightmares during the pandemic increased the likelihood of reporting SI (OR = 1.57, 95% CI 1.20-2.05), while frequent dream recall during the pandemic served a protective effect (OR = 0.74, 95% CI 0.59-0.94). These findings have important implications for identifying those at risk of suicide and may offer a potential pathway for suicide prevention.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".