The Implication of the First Wave of COVID-19 on Mental Health: Results from a Portuguese Sample
Bibliographic record
Abstract
The social conditions created by the COVID-19 pandemic had a great potential to affect the mental health of individuals. Meta-analyses indicate a rise in these problems in these periods among general populations, patients and health professionals, even with substantial heterogeneous results. This paper examines mental health impacts specifically during the first wave of COVID-19. An online survey was conducted with a Portuguese convenience sample (N = 1.062) comprising questions about substance use, perceived stress, post-traumatic stress disorder and self-damage behaviors. The results concerning substance use show an extensive use of medication to sleep or calm down, especially among women and older respondents, a small percentage of alcohol consumers with a high pattern of use and less frequent cannabis consumption, even with a quarter of users who began only in the COVID-19 period. The rates of perceived stress and PTSD were higher compared with international prevalence estimations during the pandemic conditions. Both correlated measures were worse for women and young people. Another problematic issue was the rate of suicidal ideation, with a relevant proportion of starters during this period. These data reinforce the need to promote access to mental health services.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".