Have public health responses to COVID-19 considered social inequalities in health? Insights from Brazil, Canada, France & Mali
Bibliographic record
Abstract
Background: Research has indicated an increased risk of self-harm repetition and suicide among individuals with frequent self-harm episodes. Co-occurring physical and mental illness further increases the risk of self-harm and suicide. However, the association between this co-occurrence and frequent self-harm episodes is not well understood. We examined the profile of individuals with frequent self-harm episodes and the association between physical and mental illness comorbidity, self-harm repetition and highly lethal self-harm acts. Methods: The study included consecutive patients with five or more self-harm presentations to Emergency Departments across three general hospitals in the Republic of Ireland. The study included file reviews (n=183) and semi-structured interviews (n=36). Multivariate logistic regression models were used to test the association between the sociodemographic and the comorbidity variables on highly lethal self-harm acts. Thematic analysis was applied to identify themes related to the comorbidity and frequent self-harm repetition. Findings: Most of the participants were female (59.6%), single (56.1%) and unemployed (57.4%). The predominant current self-harm method was drug overdose (60%). Almost 90 % of the participants had history of a mental or behavioural disorder, and 56.8% had recent physical illness. The most common psychiatric diagnoses were alcohol use disorders (51.1%), borderline personality disorder (44.0%), and major depressive disorder (37.8%). Male gender (OR=2.89) and alcohol abuse (OR=2.64) were associated with highly lethal self-harm acts. Major qualitative themes were a) the functional meaning of self-harm b) self-harm comorbidity c) family psychiatric history and d) contacts with mental health services. Participants described experiencing an uncontrollable self-harm urge, and self-harm was referred to as a way to get relief from emotional pain or self- punishment to cope with anger and stressors. Conclusions: Physical and mental illness comorbidity was high among the participants. The mental and physical illness comorbidity of these patients should be addressed via a biopsychosocial assessment and subsequent interventions.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".