Perspectives of patients, family members, health professionals and the public on the impact of COVID-19 on mental health
Bibliographic record
Abstract
BACKGROUND: The coronavirus (COVID-19) pandemic has seen a global surge in anxiety, depression, post-traumatic stress disorder (PTSD), and stress. AIMS: This study aimed to describe the perspectives of patients with COVID-19, their family, health professionals, and the general public on the impact of COVID-19 on mental health. METHODS: A secondary thematic analysis was conducted using data from the COVID-19 COS project. We extracted data on the perceived causes and impact of COVID-19 on mental health from an international survey and seven online consensus workshops. RESULTS: We identified four themes (with subthemes in parenthesis): anxiety amidst uncertainty (always on high alert, ebb and flow of recovery); anguish of a threatened future (intense frustration of a changed normality, facing loss of livelihood, trauma of ventilation, a troubling prognosis, confronting death); bearing responsibility for transmission (fear of spreading COVID-19 in public; overwhelming guilt of infecting a loved one); and suffering in isolation (severe solitude of quarantine, sick and alone, separation exacerbating grief). CONCLUSION: We found that the unpredictability of COVID-19, the fear of long-term health consequences, burden of guilt, and suffering in isolation profoundly impacted mental health. Clinical and public health interventions are needed to manage the psychological consequences arising from this pandemic.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".