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Record W4288055735 · doi:10.2196/preprints.40989

Transgender and non-binary people’s preferences for virtual health care post-pandemic: A cross-sectional Canadian study (Preprint)

2022· preprint· en· W4288055735 on OpenAlexaboutno aff
Jose M. Navarro, Ayden I. Scheim, Greta R. Bauer

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsTransgenderPandemicHealth careCross-sectional studyPsychologyMedicineFamily medicineCoronavirus disease 2019 (COVID-19)Political science

Abstract

fetched live from OpenAlex

BACKGROUND Virtual health care use has dramatically increased in response to the COVID-19 pandemic, raising the question of its potential role post-pandemic. For transgender and non-binary (TNB) people, virtual care is promising because it may expand access to health care providers who are affirming and competent to address TNB health needs. 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 aims to identify sociodemographic, health, and social factors associated with post-pandemic virtual care preferences in TNB communities. METHODS The 2020 Trans PULSE Canada COVID Survey examined 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 post-pandemic 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 the quantitative findings. RESULTS Of 812 participants who indicated whether they would prefer virtual or in-person care post-pandemic, a weighted 32.7% (n=275) would prefer virtual care and 67.3% (n=537) would prefer in-person care. Preferences for in-person over virtual care were associated with being in the 14-19 (85.0%), 50-64 (80.0%), and 65+ (90.7%) age groups (P=.002). Preferences for virtual over in-person care were associated with having a chronic health condition (37.7% versus 29.9%; P=.03) and having probable anxiety (34.7% versus 25.7%; P=.04). Among participants with romantic partners, preferences varied based on the partner’s level of support for gender identity or expression (P=.004); participants with moderately supportive partners were more likely than participants with very supportive partners to prefer in-person care (85.1% versus 62.3%). Care preferences did not vary significantly based on 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 barriers and facilitators to virtual care access and quality. These observed differences support flexibility with options to choose between in-person and virtual modalities of health care to meet TNB patients’ specific health needs.

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)0.003
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 score0.029
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.068
GPT teacher head0.409
Teacher spread0.340 · 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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Citations0
Published2022
Admission routes1
Has abstractyes

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