Hospitalization for self-harm during the early months of the Covid-19 pandemic in France: a nationwide study
Bibliographic record
Abstract
ABSTRACT Introduction Little is known to date about the impact of Covid-19 pandemic on self-harm incidence. Methods The number of hospitalizations for self-harm in France (mainland and overseas) from January to August 2020 (which includes the first confinement from March 17 th to May 11 th ) was compared to the same period in 2019-2017. Hospital data with the ICD-10 codes X60-84 were extracted from the national administrative database (PMSI). Results There were 53,583 hospitalizations for self-harm in France between January and August 2020. Compared to the same period in 2019, this represents an overall 8.5% decrease. This decrease started the first week of the confinement and the number of hospitalizations remained at lower levels relative to 2019 until the end of August. The decrease was more marked in women (−9.8%) than men (−6.4%). However, an increase in hospitalizations was observed in individuals aged 75 and older (+5.3 to +11.6%). Moreover, the number of self-harm by firearm (+20.3%), jumping from height (+10.5%), and drowning (+4.7%) increased between 2019 and 2020, as well as the number of hospitalizations in intensive care (+3.5%) and deaths at discharge from hospital (+8.0%). No correlation was found between the evolution in the number of hospitalizations for self-harm and the number of severe cases of Covid-19 (hospitalization and mortality rates) across administrative departments. Discussion During the early months of the Covid-19 pandemic in France - including the first confinement -, a general decrease in the number of hospitalizations for self-harm was observed. However, an increase was found among elderly, a population at higher Covid 19-related mortality risk, and in the number of more severe suicidal acts. These results, therefore, shed light on a complex relationship between the pandemic and self-harm occurrence. This situation may change with time, which requires active suicide prevention strategies.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".