Time Trends of the Public’s Attention Toward Suicide During the COVID-19 Pandemic: Retrospective, Longitudinal Time-Series Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has overwhelmed health care systems around the world. Emerging evidence has suggested that substantially few patients seek help for suicidality at clinical settings during the COVID-19 pandemic, which has elicited concerns of an imminent mental health crisis as the course of the pandemic continues to unfold. Clarifying the relationship between the public's attention to knowledge about suicide and the public's attention to knowledge about the COVID-19 pandemic may provide insight into developing prevention strategies for a putative surge of suicide in relation to the impact of the COVID-19 pandemic. OBJECTIVE: The goal of this retrospective, longitudinal time-series study is to understand the relationship between temporal trends of interest for the search term "suicide" and those of COVID-19-related terms, such as "social distancing," "school closure," and "lockdown." METHODS: We used the Google Trends platform to collect data on daily interest levels for search terms related to suicide, several other mental health-related issues, and COVID-19 over the period between February 14, 2020 and May 13, 2020. A correlational analysis was performed to determine the association between the search term ''suicide'' and COVID-19-related search terms in 16 countries. The Mann-Kendall test was used to examine significant differences between interest levels for the search term "suicide" before and after school closure. RESULTS: We found that interest levels for the search term "suicide" statistically significantly inversely correlated with interest levels for the search terms "COVID-19" or "coronavirus" in nearly all countries between February 14, 2020 and May 13, 2020. Additionally, search interest for the term ''suicide'' significantly and negatively correlated with that of many COVID-19-related search terms, and search interest varied between countries. The Mann-Kendall test was used to examine significant differences between search interest levels for the term "suicide" before and after school closure. The Netherlands (P=.19), New Zealand (P=.003), the United Kingdom (P=.006), and the United States (P=.049) showed significant negative trends in interest levels for suicide in the 2-week period preceding school closures. In contrast, interest levels for suicide had a significant positive trend in Canada (P<.001) and the United States (P=.002) after school closures. CONCLUSIONS: The public's attention to suicide might inversely correlate with the public's attention to COVID-19-related issues. Additionally, several anticontagion policies, such as school closure, might have led to a turning point for mental health crises, because the attention to suicidality increased after restrictions were implemented. Our results suggest that an increased risk of suicidal ideation may ensue due to the ongoing anticontagion policies. Timely intervention strategies for suicides should therefore be an integral part of efforts to flatten the epidemic curve.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.001 | 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".