Exploring Suicide-Related Internet Use Among Suicidal Mental Health Patients in the United Kingdom: Cross-Sectional Questionnaire Study
Bibliographic record
Abstract
Background: The dual nature of suicide-related internet use (SRIU) as preventative or harmful is well-documented, but its characteristics in the mental health patient population remain underresearched. Some evidence suggests mental health patients engage in SRIU differently from the general population. Objective: This study aims to explore the types, motivations, frequency, and perceived impacts of SRIU in suicidal mental health patients, as well as their engagement with web-based prevention resources. Methods: A cross-sectional study was conducted using an anonymous web-based survey distributed between June and December 2023. Participants (n=696) were UK adults with secondary mental health service contact and recent suicidal thoughts or behaviors. Of these, 523 (75%) participants engaged in SRIU. Collected data included sociodemographic details, clinical history, types and motivations for SRIU, and interactions with suicide prevention resources. Analysis used descriptive statistics, chi-square, and Wilcoxon tests, with multiple testing corrections applied. Results: The most common SRIU type was searching for suicide-related content (456/523, 87.4%), followed by connecting with others (271/523, 51.8%). Motivations included seeking information on suicide methods (313/523, 60.8%) and support for suicidality (271/523, 57.2%), with significant overlap. Participants perceived SRIU as neither harmful nor helpful overall, with those seeking suicide methods rating it as more harmful. Most participants encountered suicide prevention messaging, but less than half engaged with it. Only 27.5% (n=144) participants disclosed their SRIU to clinicians, with only 1 in 10 being asked about it by their clinician. Conclusions: This study underscores the dual role of SRIU as both a source of support and a potential risk for mental health patients. Despite high exposure to suicide prevention messaging, engagement was limited, suggesting inefficiencies in current intervention designs. Clinicians rarely inquired about SRIU, and voluntary disclosure by patients was low, representing missed opportunities for intervention. Proactive discussions about SRIU in clinical settings could improve risk identification and support planning. Addressing its harmful aspects while leveraging its potential for support requires integrated online and offline 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".