“Post-lockdown Depression”: Adaptation Difficulties, Depressive Symptoms, and the Role of Positive Solitude When Returning to Routine After the Lifting of Nation-Wide COVID-19 Social Restrictions
Bibliographic record
Abstract
Objectives: The aim of the current study was to identify difficulties in adapting to normal life once COVID-19 lockdown has been lifted. Israel was used as a case study, as COVID-19 social restrictions, including a nation-wide lockdown, were lifted almost completely by mid-April 2021, following a large-scale vaccination operation. Methods: age = 61.6 ± 12.8, range 40-85 years old) reported on return-to-routine adaptation difficulties (on a novel index), depression, positive solitude, and several demographic factors. Results: Of the participants, 40.4% met the criteria of (at least) mild depressive symptoms. Higher levels of adaptation difficulties were related to higher ratios of clinical depressive symptoms. This link was moderated by positive solitude. Namely, the association between return-to-routine adaptation difficulties and depression was mainly indicated for individuals with low positive solitude. Conclusions: The current findings are of special interest to public welfare, as adaptation difficulties were associated with higher chance for clinical depressive symptoms, while positive solitude was found to be as an efficient moderator during this period. The large proportion of depressive symptoms that persist despite lifting of social restrictions should be taken into consideration by policy makers when designing return-to-routine plans.
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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 | 0.002 |
| 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 | 0.001 | 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".