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Record W7017487033

Asian LGBT Non-Citizen Immigrants in California

2023· article· en· W7017487033 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship (California Digital Library) · 2023
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationSocioeconomic statusPovertyStressorStigma (botany)Minority stressEthnic groupPublic healthVietnamese
DOInot available

Abstract

fetched live from OpenAlex

This study used data gathered between 2015 and 2021 on the annual California Health Interview Survey to examine the demographic, socioeconomic, and health characteristics of Asian non-citizenLGBT immigrants.We focus on non-citizens 1 because they are a group at heightened vulnerability to low socioeconomic status and poor health.Information about U.S.-born Asian LGBT people and Asian non-LGBT non-citizens is presented to identify similarities and differences in the needs of these overlapping communities.Overall, Asian LGBT non-citizens were younger and less likely to be married or raising children than their non-LGBT counterparts.However, they reported higher levels of English proficiency.Almost a third of Asian LGBT non-citizens were living at less than 200% of the federal poverty level, and over a third reported not having a usual source of health care.For those with low incomes, half reported food insecurity. KEY FINDINGS• More than half of Asian LGBT non-citizens, cisgender and transgender, identified as bisexual (58.2%) and 28.4% as gay/lesbian, while some (13.4%)identified as heterosexual and were also transgender.• Among non-citizens, Asian LGBT people were younger than their non-LGBT counterparts. About two-thirds (67.7%) of AsianLGBT non-citizens were under the age of 35 compared to just over 40 percent (43.9%) of their non-LGBT counterparts.• Slightly more than half (52.0%) of all Asian LGBT non-citizens were cisgender women, about one-third (32.7%) were cisgender men, and 15.3% were transgender (of all gender identities and both sexes assigned at birth).• Among non-citizens, Asian LGBT people were less likely to be coupled and raising children than their non-LGBT counterparts.More than a quarter (28.0%) of LGBT non-citizens were married or living with a partner compared to 67.9% of their non-LGBT peers.About 7.4% ofLGBT non-citizens had kids compared to 37.8% of their non-LGBT peers.• Many Asian LGBT non-citizens are multi-lingual.Slightly more than half (51.8%) spoke one or more Asian languages at home, including Cantonese, Tagalog, Korean, and Vietnamese in addition to English.Another 26.5% spoke only languages other than English at home.Most (88.7%)LGBT non-citizens indicated they spoke English well or very well.However, more than one in ten (11.3%) reported not speaking English well.• Although most (85.5%)Asian LGBT non-citizens were in the workforce, almost a third (31.0%) were living at less than 200% of the federal poverty level.1 Non-citizens include those who do not have authorization ("documentation") from the U.S. government to be in the country, as well as those "authorized" to be in the U.S., including people who have a Permanent Resident Card ("Green Card"), work or student visas, and those seeking or who have received asylee or refugee status from U.S. Citizenship and Immigration Services. Asian LGBT Non-Citizen Immigrants in California | 3• While half (50.3%) of Asian LGBT non-citizens living at less than 200% of the federal poverty level were food insecure, relatively few (17.9%) were enrolled in the CalFresh food stamp benefits program.• More Asian LGBT non-citizens were experiencing psychological distress than their non-LGBT non-citizen peers (26.8% vs. 4.6%, respectively).• Over a third (38.8%) of LGBT non-citizens reported that they did not have a usual source of health care-more than twice the proportion of U.S.-born LGBT peers who said that they did not have a usual source of care (14.6%).These findings indicate a need to address the socioeconomic and health challenges faced by AsianLGBT non-citizens, including poverty, food insecurity, barriers accessing health care, and higher rates of psychological distress.The findings also indicate a need to increase enrollment in primary

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)0.000
metaresearch head score (Gemma)0.000
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 score0.480
Threshold uncertainty score0.954

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.021
GPT teacher head0.285
Teacher spread0.263 · 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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