Suicide-related internet use among mental health patients who died by suicide in the UK: a national clinical survey with case–control analysis
Bibliographic record
Abstract
Background: Suicide-related internet use (SRIU) has been shown to be linked to suicide. However, there is limited research on SRIU among mental health patients, who are at 4 to 7 times increased risk of suicide compared to the general population. This study aims to address this gap by exploring the prevalence of SRIU among mental health patients who died by suicide in the UK and describing their characteristics. Methods: The study was carried out as part of the National Confidential Inquiry into Suicide and Safety in Mental Health (NCISH). Data were collected on sociodemographic, clinical, suicide characteristics and engagement in SRIU of patients who died by suicide between 2011 and 2021. The study utilised a case-control design to compare patients who engaged in suicide-related internet use with those who did not. Findings: The presence or absence of SRIU was known for 9875/17,347 (57%) patients; SRIU was known to be present in 759/9875 (8%) patients. The internet was most often used to obtain information on suicide methods (n = 523/759, 69%) and to visit pro-suicide websites (n = 250/759, 33%) with a significant overlap between the two (n = 152/759, 20%). Engaging in SRIU was present across all age groups. The case-control element of the study showed patients who were known to have engaged in SRIU were more likely to have been diagnosed with autism spectrum disorder (OR = 2.13, 95% CI: 1.43-3.18), have a history of childhood abuse (OR = 1.70, 95% CI: 1.36-2.13) and to have received psychological treatment (OR = 1.43, 95% CI: 1.18-1.74) than controls. Additionally, these patients were more likely to have died on or near a salient date (OR = 2.11, 95% CI: 1.61-2.76), such as a birthday or anniversary. Interpretation: The findings affirm SRIU as a feature of suicide among patients of all ages and highlight that clinicians should inquire about SRIU during assessments. Importantly, as the most common type of SRIU can expand knowledge on suicide means, clinicians need to be aware of the association between SRIU and choice of methods. This may be particularly relevant for patients approaching a significant calendar event. Funding: The Healthcare Quality Improvement Partnership.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".