Factors associated with suicidal ideation in the French nationwide EPICOV study
Bibliographic record
Abstract
Introduction The COVID-19 pandemic seems associated with a worsening in mental health issues as well as a widening of pre-existing social and health inequalities. Assessment of its impact on suicidal behavior might therefore be relevant. Objectives To assess factors associated with suicidal ideation in the general population, using data from the French nationwide Epicov study Methods In the nationally representative Epicov study, data on occurrence of suicidal ideation from November 2019 to November 2020 were available, including timing with respect to France’s lockdown periods. We studied the incidence of suicidal ideation among participants without a prior history of suicidal behavior, after May 11, 2020, when first COVID-19 related lockdown was suspended. Studied factors assessed sociodemographic and health status, including mental health, as well as COVID-19 related information such as symptom. Associations between selected factors and suicidal ideation were assessed in sex-stratified adjusted logistic regression models. Results In the Epicov study, prevalence of suicidal ideation was of 2,9%, as opposed to around 4% pre-pandemic. Among 48 702 female and 41 016 male participants, health or financial issues were associated with a higher incidence of suicidal ideation. Interestingly, Covid19-like symptoms were also associated with higher risk of suicidal ideation. While an impaired mental health has been observed in survivors of past pandemics, given the unprecedented context of the current pandemic, this association needs further investigation. Conclusions While the COVID-19 pandemic might have lowered suicidal ideation, it’s aftermaths may reverse the trend. To prevent this rise, identification of vulnerable groups is crucial to promote tailored public health strategies. Disclosure No significant relationships.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".