What are the mental health changes associated with the COVID-19 pandemic in people with medical conditions? An international survey
Bibliographic record
Abstract
OBJECTIVES: The COVID-19 pandemic has negatively impacted mental health worldwide but there is paucity of knowledge regarding the level of change in mental health in people with a medical condition (physical/psychiatric). The objectives of this study were (1) to compare the change in mental health in people with and without medical conditions, (2) to assess the change in various types of medical conditions, (3) to evaluate the association between change in mental health and number of comorbidities, and (4) to investigate the influence of receiving treatment and activity limitation imposed by the medical condition(s). DESIGN: Cross-sectional. SETTING: Online international survey. PARTICIPANT: English-speaking adults (age ≥18) were included in the study, with no exclusions based on sex/gender or location. 1276 participants (mean age 30.4, 77.7% female) were included. PRIMARY AND SECONDARY OUTCOME MEASURES: Pre and during COVID-19 pandemic symptoms of anxiety (Generalized Anxiety Disorder-2) and depression (Patient Health Questionnaire-9) were assessed. The Self-Administered Comorbidity Questionnaire was used to collect data regarding medical conditions.Repeated-measures analysis of covariance (objectives 1, 2 and 4) and Pearson's correlation coefficient (objective 3). RESULTS: 50.1% of participants had a medical condition. During the COVID-19 pandemic, compared with people with no medical condition, people with both psychiatric and physical conditions experienced significantly higher symptoms of anxiety (12%, p=0.009) and depression (9.4%, p<0.001). Although not statistically significant, the increase in anxiety and depression occurred across seven major categories of conditions. An association was found between having a higher number of medical conditions with higher anxiety and depression symptoms (r=0.16 anxiety, r=0.14 depression, p<0.001). Receiving treatment and being functionally limited by the disease did not have a significant impact on the amount of change (p>0.05). CONCLUSIONS: During the COVID-19 pandemic, people who had a combination of psychiatric and physical conditions experienced greater symptoms of anxiety and depression. Patients with chronic diseases may need extra support to address their mental health as a result of the 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".