Impact of the COVID-19 Pandemic on Online Obsessive-Compulsive Disorder Support Community Members: Survey Study
Bibliographic record
Abstract
BACKGROUND: People with obsessive-compulsive disorder (OCD) have faced unique challenges during the COVID-19 pandemic. Research from the first two months of the pandemic suggests that a small proportion of people with OCD experienced worsening in their OCD symptoms since the pandemic began, whereas the rest experienced either no change or an improvement in their symptoms. However, as society-level factors relating to the pandemic have evolved, the effects of the pandemic on people with OCD have likely changed as well, in complex and population-specific ways. Therefore, this study contributes to a growing body of knowledge on the impact of the COVID-19 pandemic on people and demonstrates how differences across studies might emerge when studying specific populations at specific timepoints. OBJECTIVE: This study aimed to assess how members of online OCD support communities felt the COVID-19 pandemic had affected their OCD symptoms, around 3 months after the pandemic began. METHODS: We recruited participants from online OCD support communities for our brief survey. Participants indicated how much they felt their OCD symptoms had changed since the pandemic began and how much they felt that having OCD was making it harder to deal with the pandemic. RESULTS: We collected survey data from June through August 2020 and received a total of 196 responses, some of which were partial responses. Among the nonmissing data, 65.9% (108/164) of the participants were from the United States and 90.5% (152/168) had been subjected to a stay-at-home order. In all, 92.9% (182/196) of the participants said they experienced worsening of their OCD symptoms since the pandemic began, although the extent to which their symptoms worsened differed across dimensions of OCD; notably, symmetry and completeness symptoms were less likely to have worsened than others. Moreover, 95.5% (171/179) of the participants felt that having OCD made it difficult to deal with the pandemic. CONCLUSIONS: Our study of online OCD support community members found a much higher rate of OCD symptom worsening than did other studies on people with OCD conducted during the current COVID-19 pandemic. Factors such as quarantine length, location, overlapping society-level challenges, and differing measurement and sampling choices may help to explain this difference across studies.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".