The Preferences of Transgender and Nonbinary People for Virtual Health Care After the COVID-19 Pandemic in Canada: Cross-sectional Study
Notice bibliographique
Résumé
Background Virtual health care use has dramatically increased in response to the COVID-19 pandemic, raising the question of its potential role after the pandemic. For transgender (trans) and nonbinary (TNB) people, virtual care is promising because it may expand access to appropriate health care providers. However, emerging research indicates potential disparities in virtual care access related to sociodemographic, health, and social factors. There is a paucity of research on the factors affecting patient preferences for virtual versus in-person care, particularly in TNB communities. Objective This study aimed to identify the sociodemographic, health, and social factors associated with postpandemic virtual care preferences in TNB communities. Methods The 2020 Trans PULSE Canada COVID survey examined the health, social, and economic impacts of the COVID-19 pandemic among 820 TNB participants who previously completed the prepandemic 2019 Trans PULSE Canada survey (n=2783). Data were weighted to the demographics of the 2019 sample. Chi-square tests were used to compare postpandemic preferences for virtual versus in-person care across sociodemographic, health, and social characteristics. Participants provided open-text responses explaining their preferences, which were used to contextualize quantitative findings. Results Among 812 participants who indicated whether they would prefer virtual or in-person care after the pandemic, a weighted 32.7% (n=275) would prefer virtual care and 67.3% (n=537) would prefer in-person care. Preference for in-person over virtual care was associated with being in the 14-19 (49/56, weighted 85.0%), 50-64 (51/62, weighted 80.0%), and ≥65 (9/10, weighted 90.7%) age groups (χ25=19.0; P=.002). Preference for virtual over in-person care was associated with having a chronic health condition (125/317, weighted 37.7% versus 150/495, weighted 29.9%; χ21=4.7; P=.03) and having probable anxiety (229/645, weighted 34.7% versus 46/167, weighted 25.7%; χ21=4.3; P=.04). Among participants with romantic partners, preferences varied based on the partner’s level of support for gender identity or expression (χ23=13.3; P=.004). Participants with moderately supportive partners were more likely than participants with very supportive partners to prefer in-person care (36/43, weighted 85.1% versus 275/445, weighted 62.3%). Care preferences did not vary significantly based on the indicators of socioeconomic status. Open-text responses showed that multiple factors often interacted to influence participant preferences, and that some factors, such as having a chronic condition, simultaneously led some participants to prefer virtual care and others to prefer in-person care. Conclusions TNB people may have differential interest in virtual care based on factors including age, chronic and mental health conditions, and gender-unsupportive home environments. Future research examining virtual care preferences would benefit from mixed methods intersectional approaches across these factors, to explore complexity in the barriers and facilitators of virtual care access and quality. These observed differences support flexibility with options to choose between in-person and virtual health care to meet TNB patients’ specific health needs.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».