Psychological symptoms, mental fatigue and behavioural adherence after 72 continuous days of strict lockdown during the COVID-19 pandemic in Argentina
Bibliographic record
Abstract
BACKGROUND: An early and prolonged lockdown was adopted in Argentina during the first wave of COVID-19. Early reports evidenced elevated psychological symptoms. AIMS: To explore if the prolonged lockdown was associated with elevated anxiety and depressive symptoms; if mental fatigue was associated with lockdown adherence (a phenomenon called 'behavioural fatigue'); and if financial concerns were associated with lockdown adherence and emotional symptoms. METHOD: The survey included standardised questionnaires to assess depressive (PHQ-9) and anxious (GAD-7) symptoms, mental fatigue, risk perception, lockdown adherence, financial concerns, daily stress, loneliness, intolerance to uncertainty, negative repetitive thinking and cognitive problems. LASSO regression analyses were carried out to predict depression, anxiety and lockdown adherence. RESULTS: The survey reached 3617 adults (85.2% female) from all provinces of Argentina after 72 days of lockdown. Data were collected between 21 May 2020 and 4 June 2020. In that period, Argentina had an Oxford stringency index of 85/100. Of those surveyed, 45.6% and 27% met the cut-offs for depression and anxiety, respectively. Mental fatigue, cognitive failures and financial concerns were correlated with psychological symptoms, but not with adherence to lockdown. In regression models, mental fatigue, cognitive failures and loneliness were the most important variables to predict depression, intolerance to uncertainty and lockdown difficulty were the most important for anxiety, and perceived threat was the most important for predicting lockdown adherence. CONCLUSIONS: During the extended lockdown, psychological symptoms increased, being enhanced by mental fatigue, cognitive difficulties and financial concerns. We found no evidence of behavioural fatigue. Thus, feeling mentally fatigued is not the same as being behaviourally fatigued.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".