Effectiveness of an eHealth Intervention for Reducing Psychological Distress and Increasing COVID-19 Knowledge and Protective Behaviors among Ethnoracially Diverse Sexual and Gender Minority Adults: A Quasi-experimental Study (#SafeHandsSafeHearts)
Bibliographic record
Abstract
Abstract Purpose Lesbian, gay, bisexual, transgender, queer, and other persons outside of heteronormative and cisgender identities (LGBTQ+) and ethnic/racial minority populations are at heightened vulnerability amid the Covid-19 pandemic. Systemic marginalization and resulting adverse social determinants of health contribute to health disparities among these populations that result in more severe consequences due to Covid-19 and the public health measures to control it. We developed and tested a tailored online intervention (#SafeHandsSafeHearts) to support ethnoracially diverse LGBTQ+ individuals in Toronto, Canada amid the pandemic. Methods We used a quasi-experimental pre-test post-test design to evaluate the effectiveness of a 3-session, peer-delivered eHealth intervention in reducing psychological distress and increasing Covid-19 knowledge and protective behaviors. Individuals ≥18-years-old, resident in Toronto, and self-identified as sexual or gender minority were recruited online. Depressive and anxiety symptoms, Covid-19 knowledge and protective behaviors were assessed at baseline, 2-weeks postintervention, and 2-months follow-up. We used generalized estimating equations and zero-truncated Poisson models to evaluate the effectiveness of the intervention on the four primary outcomes. Results From March to November 2021, 202 participants (median age, 27 years [Interquartile rage: 23-32]) were enrolled in #SafeHandsSafeHearts. Over half (54%, n=110) identified as cisgender lesbian or bisexual women or women who have sex with women, 26.2% (n=53) cisgender gay or bisexual men or men who have sex with men, and 19.3% (n=39) transgender or nonbinary individuals. The majority (75.7%, n=143) were Black and other people of color. The intervention led to statistically significant reductions in the prevalence of clinically significant depressive and anxiety symptoms, and increases in Covid-19 protective behaviors from baseline to postintervention. Conclusion We demonstrated the effectiveness of a brief, peer-delivered eHealth intervention for ethnoracially diverse LGBTQ+ communities in reducing psychological distress and increasing protective behaviors amid the Covid-19 pandemic. Implementation through community-based health services with trained peer educators supports feasibility, acceptability, and the importance of engaging ethnoracially diverse LGBTQ+ communities in pandemic response preparedness. This trial is registered with ClinicalTrials.gov, number NCT04870723 .
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".