Prevalence and Sociodemographic Correlates of Unmet Need for Mental Health Counseling Among Adults During the COVID-19 Pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it