Using patient-reported outcome measures to assess psychological well-being in a non-representative US general population during the COVID-19 pandemic
Bibliographic record
Abstract
PURPOSE: The impact of the COVID-19 pandemic on psychological well-being will likely be long-lasting. Efforts directed towards monitoring the onset and progression of distress and mental health disorders are needed to identify and prioritize at-risk populations. This study assesses the psychological well-being of the United States (US) general population during the early phase of the COVID-19 COVID-19 pandemic using validated patient-reported outcome measures (PROMs). METHODS: A cross-sectional study design was used. Adults (18 years or older) who could read and write in English were recruited through Prolific in May 2020. Participants completed a REDCap survey including demographic and health-related questions and three PROMs measuring global health (PROMIS-10 Global Health), anxiety [Generalized Anxiety Disorder Scale-7 (GAD-7)], and depression [Patient Health Questionnaire-9 (PHQ-9)]. A multivariable linear regression was used to identify key factors associated with worse psychological well-being. RESULTS: Mean age of the 2023 participants was 31.92 ± 11.57 years (range, 18-82). Participants were mainly White (64.7%, n = 1309), female (52.2%, n = 1057), working full-time before the pandemic (43.5%, n = 879), and completed a college, trade, or university degree (40.7%, n = 823). Most participants reported mild to severe anxiety (57.3%, n = 1158) and depression (60%, n = 1276) on the GAD-7 and PHQ-9, respectively. Patient characteristics associated with worse psychological well-being included: age ≤ 39 years, non-White, female or gender diverse, BMI ≥ 30, uninsured, annual income ≤ $49,999 (USD), lower educational attainment, and belief that COVID-19 is deadlier than flu. CONCLUSION: PROMs can be used to assess and monitor psychological well-being during the COVID-19 pandemic and to inform the planning and delivery of targeted public health interventions to support at-risk populations.
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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 | 0.004 | 0.008 |
| 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) | 0.001 | 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 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".