The effect of the quality of the national health security systems in 12 countries on the prevalence of suicide crisis syndrome during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Limited access to health services and overwhelmed healthcare systems created a challenging environment for those in need of mental health support during the COVID-19 pandemic, and the pandemic impacted suicide risk in several ways. AIMS: The present study aimed to analyse how the quality of the health security systems in 12 countries affected suicide crisis syndrome (SCS) during the pandemic. We hypothesised that countries with robust health systems were better able to respond to the increased demand for (mental) health support, resulting in fewer cases of SCS. METHOD: From June 2020 to September 2021, 11 848 participants from 12 different countries took part in an online survey. Besides asking about sociodemographic information, the survey assessed the severity of SCS with the Suicide Crisis Inventory (SCI). The Global Health Security Index and the Legatum Prosperity Health Index were used to operationalise the quality of the national health systems. Multilevel analyses were performed to evaluate the impact of health system quality and COVID-19-associated factors on SCI scores. RESULTS: SCS was more prevalent among participants with COVID-19 symptoms and in countries with high rates of COVID-19-associated deaths. Multilevel analyses revealed a significant interaction effect of COVID-19 symptoms and national health indices. SCS occurred significantly less frequently in participants with COVID-19 symptoms living in countries with good health security systems. CONCLUSIONS: The challenges posed by the pandemic highlight the necessity to promote accessible and affordable health services to mitigate the negative impact of the pandemic on suicidal ideation and behaviour.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".