Monitoring the effects of <scp>COVID</scp>‐19 in emerging adults with pre‐existing mood and anxiety disorders
Bibliographic record
Abstract
AIM: The COVID-19 quarantine closed many mental health services. Emerging adults with pre-existing mood or anxiety disorders were of concern for worsening symptoms. We sought to demonstrate a method for monitoring mental health status of a group of patients with reduced access to their usual mental health services during quarantine. METHODS: A total of 326 patients enrolled in the First-Episode Mood and Anxiety Program in London, Ontario, Canada were invited to participate in online questionnaires regularly. Patients were flagged for high level of risk based on depression scores, suicidal ideation and worsening in anxiety, depression or quality of health. All patients were also asked if they wanted contact with a clinician. RESULTS: One hundred and fourteen (35%) patients completed at least one questionnaire. Thirty were flagged based on scores; 37 (32.5%) participating patients requested help. Participants who were flagged for concerning scores were younger, more likely to be on the wait list for treatment, to have been laid off from work and have more functional impairment. Participants requesting support had higher symptom scores for depression and lower scores on quality of health. CONCLUSIONS: The process utilized here identified patients at risk and in need of clinical support in the context of pandemic quarantine. It provided an accessible avenue for invited patients to communicate both symptom status and need for contact. Such a process can provide valuable monitoring during times when the usual communications between patients and health care providers is compromised and clinician time is limited. It is easily implemented.
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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 | 0.005 |
| 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 | 0.000 | 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".