Impact of a Virtual Care Navigation Service on Member-Reported Outcomes Among Lesbian, Gay, Bisexual, Transgender, and Queer Populations: Case Study
Notice bibliographique
Résumé
Background: While the significance of care navigation in facilitating access to health care within the lesbian, gay, bisexual, transgender, queer, and other (LGBTQ+) communities has been acknowledged, there is limited research examining how care navigation influences an individual's ability to understand and access the care they need in real-world settings. By analyzing private sector data, we can bridge the gap between theoretical research findings and practical applications, ultimately informing both business strategies and public policy with evidence grounded in real-world efficacy. Objective: The objective of this study was to evaluate the impact of specialized virtual care navigation services on LGBTQ+ individuals' ability to comprehend and access necessary care within a national cohort of commercially insured members. Methods: This case study is based on the experience of commercially insured members, aged 18 or older, who used the LGBTQ+ Health Care Navigation (LGBTQ+ Navigation) service by Included Health between January 26 and July 31, 2023. Care coordinators assisted members by connecting them with vetted identity-affirming in-network providers, helping them navigate and understand their LGBTQ+ health benefits, and providing education and advocacy for clinical and nonclinical needs. We examined the impact of navigation on 5 member-reported outcomes. In addition to reporting the proportion who agreed or strongly agreed, we calculated an impact score that averaged assigned numerical values to all 5 question responses (1=strongly disagree to 5=strongly agree) for each respondent. We used ANOVA with Tukey post hoc tests and t tests to explore the relationships between the impact score and member characteristics, including optional self-reported demographics. Results: Out of 4703 LGBTQ+ Navigation cases, 7.53% (n=354) had member-reported outcomes. A large majority of LGBTQ+ members agreed or strongly agreed that care navigation resulted in less stress (315/354, 89%), less care avoidance (305/354, 86.2%), higher confidence in finding an identity-affirming provider (327/354, 92.4%), improved ability to comprehend health care information (312/354, 88.1%), and improved ability to engage with providers (308/354, 87%). The average impact score was 4.44 (SD 0.69), with statistically significant differences by gender identity (P=.003), race (P=.01), ethnicity (P=.008), and pronouns (P=.02). The scores were highest for members with multiple gender identities (mean 4.56, SD 0.37), and members who did not provide their race, ethnicity, or their pronouns (mean 4.55, SD 0.64). Impact scores were lowest for transgender members (mean 4.11, SD 0.95). Conclusions: The LGBTQ+ Navigation service, by enhancing members' comprehension and use of necessary care, demonstrates potential public health utility and value. Continuous evaluation of navigation services can serve as a supplementary tool for employers seeking to promote health equity and improve belonging among employees. This is particularly important as discrimination and stigma against LGBTQ+ communities persist in the United States. Therefore, scalable and system-level changes that use navigation services are essential to reach a larger proportion of the LGBTQ+ population.
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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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».