Online Racism, Digital Mental Health Tools, and Online Mental Health Communication Among Black Young Adults With and Without Depression or Anxiety: Cross-Sectional Quantitative Study
Bibliographic record
Abstract
Background: Use of technological resources that provide support for mental health (ie, digital mental health tools) and opportunities to use the internet to communicate with others or receive information about mental health (ie, online mental health communication) are growing in popularity among young adults (aged 18-29 y). However, whether exposure to the negative experience of racism online is associated with the use of digital mental health tools and online mental health communication remains an important empirical question for Black young adults, given their frequent online use and engagement. Objective: This study sought to examine (1) how the frequency of exposure to online racism is associated with the use of digital mental health tools and engagement in online mental health communication and (2) how these associations differ for Black young adults with either anxiety and depression versus those without. Methods: Conducted from July to September 2024, data came from a larger cross-sectional study of 1005 monoracial Black young adults (mean age 24.07, SD3.04 y; 50.6% women) who completed an online survey and self-reported measures of exposure to online racism, use of digital mental health tools, frequency of engaging in online mental health communication, and anxiety and depressive symptoms. Two separate path analysis regression models were conducted for the outcomes of depression and anxiety. Results: Our results showed that more frequent exposure to online racism was associated with a greater likelihood of using digital mental health tools (odds ratio [OR] range 1.72-1.84; P<.001) and a greater engagement in online mental health communication (β range=.31-.36; P<.001). Those with depression and anxiety also had a greater likelihood of using digital mental health tools (depression OR 2.02; P=.001; and anxiety OR 1.71, P=.005) and a greater engagement in online mental health communication (depression β=.21; P<.001; and anxiety β=.16; P<.001). Neither anxiety nor depression was a significant moderator. Conclusions: Exposure to online racism, digital mental health tools use, and online mental health communication are linked for Black young adults. Black young adults may use digital mental health tools and engage in mental health communication online when they experience online racism or may experience online racism when they use these tools and engage online, necessitating further longitudinal analyses of these relationships. Complementary digital intervention implementation strategies that support mental health while mitigating online racism are needed. Ensuring that digital tools and mental health communications opportunities are safe, culturally relevant, and free from online racism is a set of mutually reinforcing priorities for digital interventions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".