Hospitalization for self-harm during the early months of the COVID-19 pandemic in France: A nationwide retrospective observational cohort study
Bibliographic record
Abstract
Little is known to date about the impact of COVID-19 pandemic on self-harm. The number of hospitalizations for self-harm (ICD-10 codes X60-X84) in France from 1st January to 31st August 2020 (including a two-month confinement) was compared to the same periods in 2017–2019. Statistical methods comprised Poisson regression, Cox regression and Student's t-test, plus Spearman's correlation test relating to spatial analysis of hospitalizations. There were 53,583 self-harm hospitalizations in France during January to August 2020. Compared to the same period in 2019, this represents an overall 8·5% decrease (Relative Risk [95% Confidence Interval] = 0·91 [0·90–0·93]).This decrease started in the first week of confinement and persisted until the end of August. Similarly, decrease was found in both women (RR=0·90 [0·88–0·92]) and men (RR=0·94 [0·91–0·95]), and in all age groups, except 65 years and older. Regarding self-harm hospitalizations by means category, increases were found for firearm (RR=1·20 [1·03–1·40]) and for jumping from heights (RR=1·10 [1·01–1·21]). There was a trend for more hospitalizations in intensive care (RR=1·03 [0·99–1·07]). The number of deaths at discharge from hospital also increased (Hazard Ratio = 1·19 [1·09–1·31]). Self-harm hospitalizations were weakly correlated with the rates of hospitalization for COVID-19 across administrative departments (Spearman's rho =-0·21; p = 0·03), but not with overall hospitalizations. The COVID-19 pandemic had varied effects on self-harm hospitalizations during the early months in France. Active suicide prevention strategies should be maintained. French National Research Agency.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".