Trends in medically serious suicide attempts before and after COVID-19: a four-year retrospective analysis (2018–2022)
Bibliographic record
Abstract
BACKGROUND: Suicide has become a first-order public health concern after the negative impact of COVID-19 on the general population's mental health. Several studies have analyzed the trends in suicide attempts (SA) before and after the onset of the pandemic, but few studies focus on the impact of the pandemic on medically serious suicide attempts (MSSA). METHODS: Participants were 385 hospitalized individuals ≥ 16 years old who made MSSA identified retrospectively through a review of e-medical records between 2018 and 2022 ("pre-COVID-19" and "COVID" periods). The two groups were compared on sociodemographic and clinical variables using Chi-square or Exact Fisher's tests for categorical variables and a Mann-Whitney test for continuous variables. To study the variation in MSSA over time, MSSA were aggregated monthly. Joinpoint regression analyses were used to assess time trends. RESULTS: A sample of 161 MSSA patients, 80 women and 81 men, were selected from 385 admissions after a suicide attempt (SA) in the four years (n = 160 pre-COVID period vs. n = 225 COVID period) (OR = 1.41; CI 95% = 1.0003-1.7223, p < 0.001). Sixty-eight patients with MSSA were admitted during the first period, and 93 during the COVID period (OR = 1.4 ; CI 95% = 1-1.9 ; p < 0.05). MSSA patients were more likely to be admitted to an intensive care unit during the COVID period than during the pre-COVID period (OR = 3.5; CI 95% = 1.7-6.9; p < 0.001). CONCLUSIONS: This study highlights the need for research on suicide risk during and after crisis periods, such as the COVID-19 pandemic. It provides valuable knowledge on the incidence of SA needing hospitalization, MSSA, and highly severe MSSA for four years before and after the pandemic onset.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".