Bibliographic record
Abstract
Introduction The COVID-19 pandemic has greatly disrupted the day-to-day life of university students, as it has for the general population. University students have been reported to have high rates of mental health concerns, including suicidal ideation. Objectives Ascertaining the correlation of Covid-19 dissemination and proximity to University students in Vancouver, Canada, with suicidal ideation and suicidal plan. Methods We analyuzed weekly cross-sectional data from our Canadian World Mental Health International College Student survey by plotting the 30-day suicide ideation as a binary and the ordered 30-day suicide ideation outcomes using logistic and ordered generalized additive model (GAM) respectively, with a cubic spline and adjusting for demographics. We also ran an analysis on the association between binary 30-day ideation and different sample characteristics using logistic regression. Results The time trend analysis showed that suicidal ideation did not seem to increase during the COVID-19 pandemic. On the contrary, ideation levels were found to be high in the beginning (February 2020) with a downwards trend through June to September before gradually increasing around November, 2020. We identified sociodemographic risk factors that may be associated with suicidal ideation, and established that those most at risk were students who had been emotionally overwhelmed by Covid-19 and unable to find help. Conclusions Our results seem to indicate that, in general, students have remained resilient under the stress factors presented by the pandemic, and that trends in suicidality seem to follow seasonal or school calendar year stressors rather than respond to the pandemic. However, certain subpopulations appear to be more affected than others. Disclosure No significant relationships.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".