Impact of the COVID‐19 pandemic on the psychological health of individuals with mental health conditions: A mixed methods study
Bibliographic record
Abstract
OBJECTIVES: This study explored how the coronavirus disease 2019 (COVID-19) pandemic has affected individuals with mental health conditions. METHODS: Participants were 477 adults (82% female) who reported a past-year mental health condition. They completed an online survey that included an open-ended question. Mixed methods analysis was conducted. RESULTS: While all mental health conditions were moderately impacted by the COVID-19 pandemic, self-reported impact on anxiety disorder and obsessive-compulsive disorder symptoms was greater than for all other mental health symptoms. Thematic analysis revealed five themes: (1) the contribution of the pandemic to worsening mental health; (2) life interruptions due to the pandemic; (3) increased loneliness/isolation; (4) upsides of the pandemic; and (5) normalization of the anxieties previously experienced by those with mental health conditions. CONCLUSION: Individuals with pre-existing mental health conditions reported a worsening of symptoms during the COVID-19 pandemic. Governments and organizations must focus on supporting and increasing access to treatment for this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".