Suicidal ideation across three waves of the COVID-19 pandemic in Denmark – identifying vulnerable subgroups using COH-FIT data
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic and related restrictions might have increased suicidal ideation and behaviors. This study measured suicidal ideation and associated factors in the general Danish adult population during the COVID-19 pandemic. METHODS: Using repeated cross-sectional Danish adult data from The Collaborative Outcomes study on Health and Functioning during Infection Times (COH-FIT, online anonymous survey, three waves in Denmark, including also representative samples), we estimated self-reported suicidal ideation prevalences during the three waves, and their association with demographic, socioeconomic, and psychological characteristics in multivariable analyses for each wave and pooled across all waves. RESULTS: We included 2286, 1041, and 1039 individuals (age = 18-86, females = 52 %-55 %) from waves I (1-31/05/2020), II (11-20/01/2021), and III (14-20/01/2022). The prevalence of suicidal ideation was higher in wave I (42 %) than in waves II (30 %, p < 0.001) and III (28 %, p < 0.001). Odds for suicidal ideation were higher in those with loneliness, financial loss and prior suicidal attempts consistently across all three waves (range OR = 4.91; 95%CI = 4.02-5.99 to OR = 1.84; 95%CI = 1.42-2.39). Also, men, respondents with low and medium educational level, and those with a history of mental illness had higher odds of suicidal ideation, yet not consistently across waves. In multivariable analyses across pooled waves, suicidal ideation was associated (in descending order) with loneliness, financial loss, low-medium educational level, prior attempted suicide, history of mental illness, male sex, and younger age. CONCLUSION: COH-FIT Danish data identified groups vulnerable to suicidal ideation, especially early during the pandemic, with specific demographic, socioeconomic, and psychological characteristics, calling for targeted early interventions during crisis times.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".