The impact of depression and childhood maltreatment experiences on psychological adaptation from lockdown to relaxation periods during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has presented a significant challenge to societal mental health. Yet, it remains unknown which factors influence the mental adaptation from lockdown to subsequent relaxation periods, particularly for vulnerable groups. This study used smartphone-based monitoring to explore how 74 individuals with major depression (MDD) and 77 healthy controls (HCs) responded to the transition from lockdown to relaxation during the first wave of the COVID-19 pandemic (March 21 to November 01, 2020) regarding interpersonal interactions, COVID-19-related fear (fear of participants’ own health, the health of close relatives, and the pandemics’ economic impact), and the feeling of isolation. Furthermore, we investigated the effect of a diagnosis of MDD and the experience of childhood maltreatment (CM) on adaptive functioning. During the transition from lockdown to relaxation, we observed an increase in direct contacts and a decrease in indirect contacts and self-perceived isolation in the study population. The diagnosis of MDD and the experience of CM moderated a maintenance of COVID-19-related fear: HCs and participants without the experience of CM showed a decrease in fear, while fear of participants with MDD and with an experience of CM did not change significantly. The finding that elevated COVID-19-related fear was sustained in vulnerable groups after lockdown measures were lifted could help guide psychosocial prevention efforts in future pandemic emergencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 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".