Measuring the Impact of COVID-19 on Siyan Mental Health Patients Using the Epidemic-Pandemic Impacts Inventory: Survey Study
Bibliographic record
Abstract
BACKGROUND: Recent research has shown that the impacts of the COVID-19 pandemic and social isolation on people's mental health are quite extensive, but there are limited studies on the effects of the pandemic on patients with mental health disorders. OBJECTIVE: The objective of this study was to assess the negative impacts of the COVID-19 pandemic on individuals who have previously sought treatment for a mental health disorder. METHODS: This study uses the newly developed Epidemic-Pandemic Impacts Inventory (EPII) survey. This tool was designed to assess tangible impacts of epidemics and pandemics across personal and social life domains. From November 9, 2020, to February 18, 2021, a total of 245 adults recruited from a mental health clinic completed the consent form and responded to the survey link from the Siyan Clinical Corporation and Siyan Clinical Research practices located in Santa Rosa, California, USA. RESULTS: We found that the least affected age group included individuals aged 75 years or older. This was followed closely by the 65- to 75-year-old age group. People with children under the age of 18 years also reported both more negative indicators associated with the pandemic and more positive indicators compared to those without children at home. Gender queer, nonconforming, and transgender individuals may also be at higher risk for more negative impacts associated with the pandemic. When respondents were assessed with regard to their mental health diagnosis, no differences were noted. Substance use also increased during the pandemic. CONCLUSIONS: In conclusion, the data collected here may serve as foundational research in the prevention, care, and treatment of mental health disorders during pandemics such as COVID-19. Populations such as those with previously diagnosed mental health disorders are particularly at risk for negative effects of pandemic-related stressors such as social isolation, especially if they have children in the household, are part of a younger age group, or have substance use disorder. Gender may also be a factor. Further, the EPII survey may prove to be a useful tool in understanding these effects. Overall, these data may be a critical step toward understanding the effects of the COVID-19 pandemic on populations with a mental health diagnosis, which may aid mental health practitioners in understanding the consequences of pandemics on their patients' overall well-being. TRIAL REGISTRATION: ClinicalTrials.gov NCT04568135; https://clinicaltrials.gov/ct2/show/NCT04568135.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".