Use of Telehealth to Address Depression and Anxiety in Low-income US Populations: A Narrative Review
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
Symptoms of anxiety and depressive disorders have been increasing substantially among adults in the United States (US) during the COVID-19 pandemic, particularly for low-income populations. Under-resourced communities have difficulties accessing optimal treatment for anxiety and depression due to costs as well as the result of limited access to health care providers. Telehealth has been growing as a digital strategy to treat anxiety and depression across the country but it is unclear how best to implement telehealth interventions to serve low-income populations. A narrative review was conducted to evaluate the role of telehealth in addressing anxiety and depression in low-income groups in the US. A PubMed database search identified a total of 14 studies published from 2012 to 2022 on telehealth interventions that focused on strengthening access to therapy, coordination of care, and medication and treatment adherence. Our findings suggest that telehealth increases patient engagement through virtual therapy and the use of primarily telephone communication to treat and monitor anxiety and depression. Telehealth seems to be a promising approach to improving anxiety and depressive symptoms but socioeconomic and technological barriers to accessing mental health services are substantial for low-income US populations.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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