Dynamic patterns of depressive symptoms and sleep during the first month of strict lockdown in two women with major depressive disorder
Bibliographic record
Abstract
This article aimed to document the day-to-day patterns of depressive symptoms and sleep parameters, and to explore the dynamic network structure of depressive symptoms during the first COVID strict lockdown. Two participants with major depressive disorder were included in a 30-days observational ecological momentary assessment study just before the lockdown in Montreal, Canada. In both cases, the self-reported depressive symptoms and core affects fluctuated during the lockdown. However, all depressive symptoms were not systematically exacerbated. Among them, significant linear and non-linear temporal patterns have been identified. For case 1, the lockdown period did not worsen the sleep quality and sleep parameters. The psychomotor retardation, fatigue, appetite loss and level of arousal played a prominent role in the idiographic dynamic symptom networks. These case studies allow a granular understanding of the lockdown effects on depressive symptoms and affective experiences dynamics, and highlight the need for person-centered mental health care to help people with major depressive disorder.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| 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 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".