COVID-19 Concerns, Information Needs, and Adverse Mental Health Outcomes among U.S. Soldiers
Bibliographic record
Abstract
INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic disrupted U.S. Military operations and potentially compounded the risk for adverse mental health outcomes by layering unique occupational stress on top of general restrictions, fears, and concerns. The objective of the current study was to characterize the prevalence of COVID-19 concerns and information needs, demographic disparities in these outcomes, and the degree to which COVID-19 concerns and information needs were associated with heightened risk for adverse mental health outcomes among U.S. Army soldiers. MATERIALS AND METHODS: Command-directed anonymous surveys were administered electronically to U.S. soldiers assigned to one of three regional commands in the Northwest United States, Europe, and Asia-Pacific Region. Surveys were administered in May to June 2020 to complete (time 1: n = 21,294) and again in December 2020 to January 2021 (time 2: n = 10,861). Only active duty or active reservists/national guard were eligible to participate. Members from other branches of service were also not eligible. RESULTS: Highly prevalent COVID-19 concerns included the inability to spend time with friends/family, social activities, and changing rules, regulations, and guidance related to COVID-19. Some information needs were endorsed by one quarter or more soldiers at both time points, including stress management/coping, travel, how to protect oneself, and maintaining mission readiness. COVID-19 concerns and information needs were most prevalent among non-White soldiers. Concerns and information needs did not decline overall between the assessments. Finally, COVID-19 concerns were associated with greater risk of multiple adverse mental health outcomes at both time points. CONCLUSIONS: COVID-19 concerns and information needs were prevalent and showed little evidence of decrement over the course of the first 6 months of the pandemic. COVID-19 concerns were consistently associated with adverse mental health outcomes. These data highlight two targets and potential demographic subgroups such that local leadership and Army medicine and public health enterprises can be better prepared to monitor and address to maintain force health and readiness in the face of possible future biomedical threats.
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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 | 0.000 |
| 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) | 0.003 | 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".