It Is Time to REACT: Opportunities for Digital Mental Health Apps to Reduce Mental Health Disparities in Racially and Ethnically Minoritized Groups
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
The behavioral health toll of the COVID-19 pandemic and systemic racism has directed increased attention to the potential of digital health as a way of improving access to and quality of behavioral health care. However, as the pandemic continues to widen health disparities in racially and ethnically minoritized groups, concerns arise around an increased reliance on digital health technologies exacerbating the digital divide and reinforcing rather than mitigating systemic health inequities in communities of color. As funding for digital mental health continues to surge, we offer five key recommendations on how the field can "REACT" to ensure the development of approaches that increase health equity by increasing real-world evidence, educating consumers and providers, utilizing adaptive interventions to optimize care, creating for diverse populations, and building trust. Recommendations highlight the need to take a strengths-based view when designing for racially and ethnically diverse populations and embracing the potential of digital approaches to address complex challenges.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it