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Record W4382722370 · doi:10.1007/s44192-023-00039-0

A cross-sectional study of the relationship between depression status, health care coverage, and sexual orientation

2023· article· en· W4382722370 on OpenAlexfundno aff
Yang Liu, Megan A. O’Grady

Bibliographic record

VenueDiscover Mental Health · 2023
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
FundersYork University
KeywordsSexual orientationDepression (economics)ReceiptLogistic regressionHealth careMental healthCross-sectional studySexual minorityMedicineOddsGerontologyPsychologyDemographyPsychiatryEnvironmental healthSocial psychology

Abstract

fetched live from OpenAlex

Health care coverage is an important factor in receipt of behavioral healthcare. This study uses data from the New York City Community Health Survey to examine how sexual minority status impacts the relationship between depression status and having health care coverage. Approximately 10% of the sample (n = 9571; 47% 45+ years old; 35% White Non-Hispanic; 7% sexual minority) reported probable depression and low health care coverage. Compared to heterosexual participants, a greater proportion of sexual minority participants had low health care coverage (17% vs. 9%) and probable depression (19% vs. 9%). Logistic regression examining the association between probable depression status and health care coverage showed that those with probable depression have odds of low health care coverage that are were 3.08 times those who did not have probable depression; this relationship was not modified by sexual orientation. Continued research to understand the interplay of health care coverage, mental health, and sexual orientation is needed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.039
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

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

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 head0.083
GPT teacher head0.472
Teacher spread0.389 · 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 teacher head, 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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