Hospitalization for self-harm during the early months of the COVID-19 pandemic in France: A nationwide retrospective observational cohort study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it