Internet Search Activity of Young People With Mood Disorders Who Are Hospitalized for Suicidal Thoughts and Behaviors: Qualitative Study of Google Search Activity
Bibliographic record
Abstract
BACKGROUND: Little is known about the internet search activity of people with suicidal thoughts and behaviors (STBs). This data source has the potential to inform both clinical and public health efforts, such as suicide risk assessment and prevention. OBJECTIVE: We aimed to evaluate the internet search activity of suicidal young people to find evidence of suicidal ideation and behavioral health-related content. METHODS: Individuals aged between 15 and 30 years (N=43) with mood disorders who were hospitalized for STBs provided access to their internet search history. Searches that were conducted in the 3-month period prior to hospitalization were extracted and manually evaluated for search themes related to suicide and behavioral health. RESULTS: A majority (27/43, 63%) of participants conducted suicide-related searches. Participants searched for information that exactly matched their planned or chosen method of attempting suicide in 21% (9/43) of cases. Suicide-related search queries also included unusual suicide methods and references to suicide in popular culture. A majority of participants (33/43, 77%) had queries related to help-seeking themes, including how to find inpatient and outpatient behavioral health care. Queries related to mood and anxiety symptoms were found among 44% (19/43) of participants and included references to panic disorder, the inability to focus, feelings of loneliness, and despair. Queries related to substance use were found among 44% (19/43) of participants. Queries related to traumatic experiences were present among 33% (14/43) of participants. Few participants conducted searches for crisis hotlines (n=3). CONCLUSIONS: Individuals search the internet for information related to suicide prior to hospitalization for STBs. The improved understanding of the search activity of suicidal people could inform outreach, assessment, and intervention strategies for people at risk. Access to search data may also benefit the ongoing care of suicidal patients.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".