Planning for a Crisis: Predicting Anxiety in a Population During COVID-19 Using Machine Learning
Bibliographic record
Abstract
COVID-19 impact on population mental health has been reported around the world. Statistics Canada has conducted a survey among Canadian population to gauge mental health challenges they experienced, specifically in terms of anxiety. We create a machine learning model to predict anxiety symptoms as measured by the General Anxiety Scale among the sample of 45,989 respondents to the survey. Eight algorithms including Logistic Regression, Random Forest, Naive Bayes, K Nearest Neighbours, Adaptive boost, Multi linear perceptron, XGBoost and LightBoost. LightBoost provided the highest performing model AUC score (AUC=87.45%). In addition, the features "perception of mental health compared to before physical distancing", "perceived life stress", and "perceived mental health" were found to be the most important three features to predict anxiety. A limitation of this study is that the sample is not representative of the Canadian population. Preparing for virtual care interventions during a crisis need to take into considerations these factors.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".