Recruiting Sexual and Gender Minority Veterans for Health Disparities Research: Recruitment Protocol of a Web-Based Prospective Cohort Study
Notice bibliographique
Résumé
BACKGROUND: The Health for Every Veteran Study is the first Veterans Health Administration-funded, nationwide study on lesbian, gay, bisexual, transgender, queer, and other sexual and gender minority (LGBTQ+) veterans' health that relies exclusively on primary recruitment methods. This study aimed to recruit 1600 veterans with diverse sexual and gender identities to study the mental health and health risk behaviors of this population. A growing body of literature highlights the health inequities faced by LGBTQ+ veterans when compared with their heterosexual or cisgender peer groups. However, there is little to no guidance in the health disparities literature describing the recruitment of LGBTQ+ veterans. OBJECTIVE: This paper provides an overview of the recruitment methodology of Health for Every Veteran Study. We describe the demographics of the enrolled cohort, challenges faced during recruitment, and considerations for recruiting LGBTQ+ veterans for health research. METHODS: Recruitment for this study was conducted for 15 months, from September 2019 to December 2020, with the goal of enrolling 1600 veterans evenly split among 8 sexual orientation and gender identity subgroups: cisgender heterosexual women, cisgender lesbian women, cisgender bisexual women, cisgender heterosexual men, cisgender gay men, cisgender bisexual men, transgender women, and transgender men. Three primary recruitment methods were used: social media advertising predominantly through Facebook ads, outreach to community organizations serving veterans and LGBTQ+ individuals across the United States, and contracting with a research recruitment company, Trialfacts. RESULTS: Of the 3535 participants screened, 1819 participants met the eligibility criteria, and 1062 completed the baseline survey to enroll. At baseline, 25.24% (268/1062) were recruited from Facebook ads, 40.49% (430/1062) from community outreach, and 34.27% (364/1062) from Trialfacts. Most subgroups neared the target enrollment goals, except for cisgender bisexual men, women, and transgender men. An exploratory group of nonbinary and genderqueer veterans and veterans with diverse gender identities was included in the study. CONCLUSIONS: All recruitment methods contributed to significant portions of the enrolled cohort, suggesting that a multipronged approach was a critical and successful strategy in our study of LGBTQ+ veterans. We discuss the strengths and challenges of all recruitment methods, including factors impacting recruitment such as the COVID-19 pandemic, negative comments on Facebook ads, congressional budget delays, and high-volume surges of heterosexual participants from community outreach. In addition, our subgroup stratification offers important disaggregated insights into the recruitment of specific LGBTQ+ subgroups. Finally, the web-based methodology offers important perspectives not only for reaching veterans outside of the Veterans Health Administration but also for research studies taking place in the COVID-19-impacted world. Overall, this study outlines useful recruitment methodologies and lessons learned to inform future research that seeks to recruit marginalized communities. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/43824.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,038 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,003 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,006 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,051 | 0,018 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».