Adrift in time: the subjective experience of circadian challenge during COVID-19 amongst people with mood disorders
Bibliographic record
Abstract
Social distancing/lockdown policies during the coronavirus (COVID-19) pandemic may alter social rhythms of people through imposition of restrictions on normal daily activities. This may in turn challenge circadian function, particularly in people with mood disorders. Although objective data describing the relationship between circadian disturbances and mood disorders exist, data regarding the subjective experience of circadian challenge is sparse, and its association with mood symptoms is unclear. The present qualitative study was one component of a mixed-methods multi-national project, which took advantage of widespread disruption to daily routines due to Government COVID-related lockdowns during 2020. The Behavior Emotion and Timing during COVID-19 (BEATCOVID) survey study included three open questions generating qualitative data on participants’ subjective experience of social disruption due to social distancing/lockdown policies, two of which asked about the barriers and opportunities for stabilizing routines. Responses were coded and analyzed using Thematic Analysis. A total of N = 997 participants responded to at least one of the free-text questions. Four themes were identified: 1) loss of daily timed activities, 2) role of social interaction, 3) altered time perception and 4) disruption to motivation and associated psychological effects. Themes were organized into a provisional heuristic map, generating hypotheses for future research centered on the new concept of ‘psychological drift.’
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".