The Effects of Self Isolation on Depression, Anxiety, and Stress Levels
Bibliographic record
Abstract
Self isolation occurs when a person is in their home or any space that is secluded for a long period of time with barely any human interaction. A study in Canada showed people were experiencing quadruple the normal levels for anxiety and double the levels of depression during the required self isolation (Dozios, 2020) brought on by the COVID-19 pandemic. The goal of this research is to determine how much self isolation affects anxiety, depression, and stress levels. The study included 30 students and community participants from Northern California. Participants were asked to complete a survey based on their emotional status during the mandatory COVID-19 self isolation. The survey is scaled like the Likert scale based on a 5 point rating 1= extremely low to 5= extremely high (McLeod, 2020). This scale is designed to allow the participants to show how much they disagree or agree with the statement. Also, the participants are asked two demographic questions at the end of the survey which are their age and gender which is optional. Social isolation in this study will be measured through a UCLA Loneliness scale (Russel, 1980). Finally, the way of measuring anxiety, depression, and stress will be found through the Depression Anxiety Stress Scales (Lovibond, 2012). The study is expected to show the negative effects that mandatory self isolation has on mental health. This research supports previous studies that show self isolation has a direct correlation with an increase in stress, anxiety, and depression levels (Taylor, 2020), and brings awareness to the issues of mental health and need for additional support to help people through this difficult time.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".