Engagement, Use, and Impact of Digital Mental Health Resources for Diverse Populations in COVID-19: Community-Partnered Evaluation
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic increased disparities for communities burdened by structural barriers such as reduced affordable housing, with mental health consequences. Limited data are available on digital resources for public mental health prevention during the COVID-19 pandemic. OBJECTIVE: The study aim was to evaluate engagement in and impact of free digital resources on the Together for Wellness/Juntos por Nuestro Bienestar (T4W/Juntos) website during COVID-19 in California. METHODS: A pilot evaluation of T4W/Juntos was performed, with partner agencies inviting providers, clients, and partners to visit the website and complete surveys at baseline (September 20, 2021, to April 4, 2022) and at 4-6-week follow-up (October 22, 2021, to May 17, 2022). Website use was assessed by three engagement items (ease of use, satisfaction, relevance), comfort in use, and use of six resource categories. Primary outcomes at follow-up were depression and anxiety (scores≥3 on Patient Health Questionnaire-2 item [PHQ2] and Generalized Anxiety Disorder-2 item [GAD2] scales). Secondary outcomes were post-pre differences in PHQ2 and GAD2 scores, and use of behavioral health hotlines and services the month before follow-up. RESULTS: Of 366 eligible participants, 315 (86.1%) completed baseline and 193 (61.3%) completed follow-up surveys. Of baseline participants, 72.6% identified as female, and 21.3% identified as lesbian, gay, bisexual, transgender, queer/questioning, and others (LGBTQ+). In terms of ethnicity, 44.0% identified as Hispanic, 17.8% as African American, 26.9% as non-Hispanic white, and 11.4% as other ethnicity. Overall, 32.7% had moderate anxiety or depression (GAD2/PHQ2≥3) at baseline. Predictors of baseline website engagement included being Hispanic versus other race/ethnicity (β=.27, 95% CI .10-.44; P=.002) and number of COVID-19-related behavior changes (β=.09, 95% CI .05-.13; P<.001). Predictors of comfort using the website were preferring English for website use (odds ratio [OR] 5.57, 95% CI 2.22-13.96; P<.001) and COVID-19-related behavior changes (OR 1.37, 95% CI 1.12-1.66; P=.002); receiving overnight behavioral health treatment in the prior 6 months (OR 0.15, 95% CI 0.03-0.69, P=.015) was associated with less comfort in website use. The main predictor of depression at follow-up (PHQ2≥3) was baseline depression (OR 6.24, 95% CI 2.77-14.09; P<.001). Engagement in T4W/Juntos was associated with lower likelihood of depression (OR 0.54, 95% CI 0.34-0.86; P=.01). Website use the month before follow-up was associated with a post-pre reduction in PHQ2 score (β=-.62, 95% CI -1.04 to -0.20; P=.004). The main predictor of GAD2≥3 at follow-up was baseline GAD2≥3 (OR 13.65, 95% CI 6.06-30.72; P<.001). Greater baseline website engagement predicted reduced hotline use (OR 0.36, 95% CI 0.18-0.71; P=.004). CONCLUSIONS: Ethnicity/language and COVID-19-related behavior changes were associated with website engagement; engagement and use predicted reduced follow-up depression and behavioral hotline use. Findings are based on participants recommended by community agencies with moderate follow-up rates; however, significance was similar when weighting for nonresponse. This study may inform research and policy on digital mental health prevention resources.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".