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Record W4412203756 · doi:10.2196/67082

Exploring the Impact of Online Mental Health Resources During the COVID-19 Pandemic on Lesbian, Gay, Bisexual, Transgender, Queer, and Questioning Adults Compared to Heterosexual Adults: Pretest-Posttest Survey Analyses

2025· article· en· W4412203756 on OpenAlexvenueno aff
Natalie Ramos, Skylar Jones, Lily Zhang, Miriam Nuño, Dannie Ceseña, Alyssa Mireles, Kenneth B. Wells

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsLesbianTransgenderMental healthPsychologyAnxietyClinical psychologyPatient Health QuestionnaireQueerDepression (economics)Sexual orientationMedicinePsychiatryDepressive symptomsSocial psychology

Abstract

fetched live from OpenAlex

Background Lesbian, gay, bisexual, transgender, queer, and questioning (LGBTQ+) individuals faced greater mental health challenges during the COVID-19 pandemic than binary-gender heterosexual (non-LGBTQ+) adults. The Together for Wellness/Juntos por Nuestro Bienestar website with free well-being resources, developed during the COVID-19 pandemic with partner input, included LGBTQ+ resources. A pilot evaluation among adults (aged ≥18 years) found engagement with and use of the website 4 to 6 weeks before follow-up was associated with reduced (pretest-posttest) depression. Results for LGBTQ+ participants were not reported. Objective This study describes baseline depression, anxiety, and website engagement for LGBTQ+ compared with non-LGBTQ+ adults and pretest-posttest changes in depression and anxiety (the primary outcome). Methods Community partners invited health and social services providers, clients, and partners to visit the website and complete a survey app (Chorus Innovations) at baseline (September 20, 2021-April 4, 2022) and a 4- to 6-week follow-up (October 22, 2021-May 17, 2022). LGBTQ+ adults were compared to non-LGBTQ+ adults in demographics, website use, depression, and anxiety. Sensitivity analyses were adjusted for nonresponse (inverse probability weighting). Regression analyses identified predictors for reduction (pretest-posttest) in depression (2-item Patient Health Questionnaire [PHQ-2]) and anxiety (2-item Generalized Anxiety Disorder scale [GAD-2]). Results Of 315 adults who completed the baseline survey and 193 who completed the follow-up survey, 64 (20.3%) and 37 (19.2%), respectively, were LGBTQ+. At baseline, LGBTQ+ compared to non-LGBTQ+ adults had higher scores on the PHQ-2 (mean 2.4, SD 1.7 vs 1.3, SD 1.3; t294=5.31; P<.001) and GAD-2 (mean 2.7, SD 1.7 vs 1.6, SD 1.5; t295=4.96; P<.001) and more COVID-19 stressors (mean score 8.1, SD 4.4 vs 6.5, SD 4.0; t298=2.8; P=.003). Before follow-up, LGBTQ+ adults had similar website use (P=.65) and likelihood to recommend the website to others (P=.26) compared to non-LGBTQ+ adults. LGBTQ+ adults had more reduction (pretest-posttest) in mean GAD-2 scores (−0.8, SD 2.0 vs 0.0, SD 1.2; t177=−3.08; P=.002) and mean PHQ-2 scores (−0.7, SD 1.7 vs −0.1, SD 1.4; t180=−2.16; P=.03) compared to non-LGBTQ+ adults. For LGBTQ+ adults, predictors of pretest-posttest decline (adjusting for nonresponse) in mean GAD-2 scores included visiting the website and using resources 4 to 6 weeks before (β=−1.95, 95% CI −3.20 to −0.70; P=.003); for decline in mean PHQ-2, visiting website/using resources had a trend as predictor that was not significant adjusting for nonresponse (β=-.94 (-2.00, 0.013), P=.09). Conclusions LGBTQ+ adults reported higher baseline depression, anxiety, and COVID-19 stressors than non-LGBTQ+ adults. Among LGBTQ+ but not among non-LGBTQ+ adults, higher website use was associated with reduced anxiety over time. Findings suggest that online resources may promote well-being for LGBTQ+ adults in pandemics.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.534
GPT teacher head0.598
Teacher spread0.064 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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