The Psychological Impact of Confinement Linked to the Coronavirus Epidemic COVID-19 in Algeria
Bibliographic record
Abstract
The COVID-19 pandemic continues to spread in countries around the world. The impact of this virus is very great on populations following the application of total and partial containment measures. Our study aims to study the psychological impact of total and partial containment applied in Algeria, on 23 March 2020, following the spread of the virus COVID-19 and also studied the habits and behaviors of the Algerian population during this new way of life and this through a cross-sectional survey launched after three days from the start of confinement to quickly assess the impacts over the period from 23 March to 12 April 2020, by an online questionnaire which allowed us to obtain 678 responses from Internet users, who live in confinement in Algeria. According to the gender variable, our sample includes 405 men, or 59.7%, and 273 women, representing 40.3%. The results of the statistical analysis carried out using SPSS version 22.0 software showed that 50.3% of the respondents were in an anxious situation during these first three weeks of confinement. In addition, 48.2% feels stressed, 46.6% of the respondents confirmed to be feeling in a bad mood, and 47.4% do not stop thinking throughout the day about this epidemic and how to protect themselves. In addition, the study shows that 87.9% of the respondents in Algeria found it difficult to follow the confinement instructions. A significant change in the habits of the population was noted especially for the time of going to bed, the time of waking up, and the use of the Internet as well as the hours devoted to daily reading.
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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 | 0.001 | 0.001 |
| 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".