Prevalence and Sociodemographic Correlates of Unmet Need for Mental Health Counseling Among Adults During the COVID-19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: This study aimed to determine the prevalence and correlates of unmet need for mental health counseling among U.S. adults during the COVID-19 pandemic. METHODS: Data from the December 9-21, 2020, cross-sectional Household Pulse Survey (N=69,944) were analyzed. RESULTS: Overall, 12.8% of adults reported an unmet need for mental health counseling in the past month, including 25.2% of adults with a positive screen for depression or anxiety. Among adults with a positive screen, risk factors associated with an unmet need for mental health counseling included female sex, younger age, income below the federal poverty line, higher education, and household job loss during the pandemic, while protective factors included Asian and Black race. CONCLUSIONS: Over one-quarter of U.S. adults with a positive screen for depression or anxiety experienced an unmet need for mental health counseling during the pandemic. Policy makers should consider increasing funding for mental health services as part of pandemic relief legislation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".