Perceived Stress among Iranians during COVID-19 Pandemic; Stressors and Coping Mechanisms: A Mixed-methods Approach
Bibliographic record
Abstract
OBJECTIVE: New coronavirus (COVID-19) pandemic socioeconomically affected the world. In this study, we measured the perceived stress in response to the COVID-19 pandemic among Iranians to determine the groups at both extremes of the spectrum followed by identifying the stressors and coping mechanisms. METHODS: This study was a mixed-methods study. We distributed a web-based 10-item perceived stress scale (PSS-10), to measure perceived stress score (PSS), through social networks from March 12 to 23, 2020. Then, we interviewed 42 students, 31 homemakers, 27 healthcare providers, and 21 male participants to identify the sources of stress and coping mechanisms. RESULTS: Finally, 13,454 participants completed the questionnaires. The median and interquartile range (IQR) of the participants' PSS was 21 (15-25). Students, homemakers, and healthcare workers (HCWs) showed a higher median (IQR) of PSS compared to other groups (23 [18 to 27], 22 [16 to 26], and 19 [14 to 24], respectively). Male participants showed a lower median (IQR) PSS (17 [12 to 23]). Content analysis of 121 participants' answers showed that the most common stressors were school-related issues mentioned by students, family-related issues mentioned by homemakers, and COVID-19-related issues mentioned by healthcare providers. Male participants' coping mechanisms were mostly related to the perception of their abilities to cope with the current crisis. CONCLUSION: Our participants clinically showed a moderate level of PSS. The main stressors among students, homemakers, and HCWs were related to their principal role in this period, and male participants' coping mechanisms were inspired by the self-image retrieved from the social perspectives.
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".