Exploring the Mental Health Needs of Persons With Autoimmune Diseases During the Coronavirus Disease 2019 Pandemic: A Proposed Framework for Future Research and Clinical Care
Bibliographic record
Abstract
Although the coronavirus disease 2019 (COVID-19) pandemic has been associated with increased psychological distress globally, it poses unique challenges to persons who are potentially more vulnerable to its effects, including patients with autoimmune disease. In this article, we review the published literature and media reports to determine factors that may contribute to mental health challenges in persons with autoimmune disease. We then explore existing mental health interventions that have been developed for use in COVID-19 and in patients with autoimmune disorders in general. We identified several potential contributors to psychological distress in patients with autoimmune disease during the pandemic, as follows: feelings of discrimination related to societal response to COVID-19, fear of infection and uncertainty related to immunosuppressive medication, diminished access to usual care and resources, previous health-related trauma, and the exacerbating effect of social isolation. Drawing from existing literature, we synthesize the identified evidence to develop a proposed framework for researching and managing mental health challenges in autoimmune disease during the pandemic and its aftermath.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| 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".