Predicting Depression in Canada by Automatic Filling of Beck’s Depression Inventory Questionnaire
Bibliographic record
Abstract
The risk for depression and anxiety increased as people adjusted to a new normal after the COVID-19 pandemic. Early detection and appropriate onset treatment and support can reduce the consequences of depression. Automatic detection of depression in social media has recently become an important area of investigation. However, because of the lack of extensive annotated data, we propose a method for using a model that learns to answer a depression questionnaire and apply it to make population-level predictions. We used the eRisk 2021 Task 3 training dataset to build an automated model to fill the Beck’s Depression Inventory (BDI) questionnaire.We selected the best performing model for each group of questions based on predefined metrics and consolidated those models into one model (called the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BDI_Multi_Model</i> ). The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BDI_Multi_Model</i> achieved better performance than the state-of-the-art for this challenging task. Then, we used this model for inference on a Canadian population dataset and compared its predictions with the statistics of the most recent mental health survey conducted by Statistics Canada. The correlation between the inference of the answered questionnaire based on our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BDI_Multi_Model</i> and the official statistics showed a strong Pearson correlation of 0.90.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".