Perceived Stress, Internet Use behaviors and Mental Health Symptoms During the Early Stages of the Pandemic in Brazil
Bibliographic record
Abstract
Abstract Purpose: This study explores perceived stress (PS), mental health (MH) symptoms and internet use behaviors during early stages of the pandemic in Brazil. Methods: A questionnaire battery was posted on various internet platforms from April to July 2020. The questionnaires included various life domains, internet use behaviors, MH symptoms, PS and quality of life, Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, Perceived Stress Scale, Obsessive Compulsive Inventory – Revised, PTSD Checklist and Quality of Life Enjoyment and Satisfaction Questionnaire. Results: Majority of participants (n=519) reported changes to financial status (71%) and social communication (89%) and various internet use behaviors (79-89%). This sample reported elevated PS (x̄=17.09, SD=8.37, 62%), mild depression (x̄=6.44, SD=5.98) and anxiety (x̄=6.62, SD=5.37) and a significant proportion had clinically significant symptoms of MDD (25%), GAD (23%), OCD (14%), PTSD (18%) and elevated PS (62%). Elevated PS was predicted by younger age (β=-0.063, p=0.005), female sex (β=0.581. p=0.05), loss of employment (β=0.665, p=0.05), healthcare worker status (β=-0.589, p=0.005), past mental health treatment (β =0.739, p=0.005) increased social media use (β=0.591, p=0.05) and increased digital information seeking (β=0.697, p=0.05). Conclusion: Mental health providers should assess internet use habits and behaviors as a potential risk factor for heightened stress.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".