Substance use, depression, and loneliness among American veterans during the COVID‐19 pandemic
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Behavioral health issues, such as substance use, depression, and social isolation, are of grave concern during COVID-19, especially for vulnerable populations. One such population is US veterans, who have high rates of pre-existing behavioral health conditions and may thus be at-risk for poorer outcomes. The current study aimed to investigate substance use among US veterans during COVID-19 as a function of pre-existing depression, loneliness, and social support. METHODS: We investigated the relationship between pre-pandemic depression and substance use during COVID-19 using linear (alcohol) and logistic (cannabis) regression among a large sample of US veterans (N = 1230). We then tested if loneliness and social support moderated these effects. RESULTS: Though there was a decrease in alcohol and cannabis use among the overall sample, veterans who screened for depression prior to the pandemic exhibited higher levels of substance use after the pandemic's onset. Loneliness compounded the effects of depression on rates of alcohol use. Social support was not protective for the effects of depression on either alcohol or cannabis use. DISCUSSION AND CONCLUSIONS: Veterans with pre-existing depression may be in need of attention for substance use behaviors. Interventions aimed at alleviating loneliness among veterans may be useful in mitigating alcohol use, but not cannabis use, amid COVID-19. SCIENTIFIC SIGNIFICANCE: Our findings are among the first to report tangible behavioral health outcomes experienced by US veterans as a result of COVID-19. Results can help inform treatment efforts for veterans who are struggling with substance use during and post-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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".