Mental Health Outcomes and Sleep Status among Patients with Coronavirus Disease 2019
Bibliographic record
Abstract
Objective: The immediate impacts of coronavirus disease 2019 (COVID-19) on mental health of affected patients and psychiatric morbidities of these patients has been neglected by researchers. We assessed mental health outcomes and sleep status among inpatients and outpatients with COVID-19 who were initially referred to our COVID-19 clinic in Mashhad, Iran during April-October 2020. Method: In this ethically approved cross-sectional study, 130 patients with confirmed COVID-19 who were referred to outpatient clinics and wards of a referral hospital in Mashhad, Iran were surveyed during April-October 2020. Demographic data were collected after obtaining informed written consent. Validated Persian versions of insomnia severity index (ISI), 9-item patient health questionnaire (PHQ-9), and 7-item generalized anxiety disorder (GAD-7) and revised impact of event scale (IES-R) were used as main outcome measures (i.e. status of anxiety, depression, insomnia, and event-related distress). Analysis was performed with SPSS using binary logistic regression. P-values < 0.05 were considered significant. Results: Overall, 65 inpatients and 65 outpatients were surveyed. The two groups did not significantly defer in terms of insomnia and depression severity, but the outpatients showed higher levels of anxiety (52.3% vs. 24.6%, P = 0.005) and distress compared to inpatients (80.0% vs. 64.6%, P < 0.001). Male sex (OR = 0.017, 95%CI = 0.000-0.708, P = 0.032) exhibited independent and inverse association with depression in COVID-19 patients. Being married (OR = 0.102, 95% CI = 0.018-0.567, P = 0.009) was independently and inversely associated with anxiety. Insomnia was independently associated with event-related distress (OR = 7.286, 95%CI = 2.017-26.321, P = 0.002). Only depression was independently associated with insomnia (OR = 49.655, 95%CI = 2.870-859.127, P = 0.007). Conclusion: We found symptoms of psychological distress and anxiety to be more prevalent among outpatients with COVD-19 than inpatients. Insomnia can be a potential risk factor for adverse mental health outcomes in these patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".