MétaCan
Menu
Back to cohort
Record W3180519793 · doi:10.3138/jmvfh-2021-0019

Perceptions of family acceptance into the military community among U.S. LGBT service members: A mixed-methods study

2021· article· en· W3180519793 on OpenAlexvenueno aff
Kathrine Sullivan, Jessica Dodge, Kathleen McNamara, Rachael Gribble, Mary Keeling, Sean Taylor-Beirne, Caroline Kale, Jeremy T. Goldbach, Nicola T. Fear, Carl A. Castro

Bibliographic record

VenueJournal of Military Veteran and Family Health · 2021
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsLesbianTransgenderMilitary serviceService (business)PsychologyPublic relationsSocial psychologyPolitical scienceGender studiesSociologyLawBusiness

Abstract

fetched live from OpenAlex

LAY SUMMARY There are approximately 16,000 families of lesbian, gay, bisexual, or transgender (LGBT) service members in the U.S. military, but very little is known about how accepted they feel in the communities in which they live. This study begins to address this question by considering the perspectives of LGBT service members, which they shared both in response to an online survey and in interviews. Findings suggest that many service members believe their spouses and families are accepted by their chain of command. However, a smaller but important group continued to express concerns about their family being accepted in their military community. Many service members appear concerned that family services available to them through the military are not appropriate for LGBT families. Altogether, this article highlights the need for more research to understand the well-being and needs of this group.

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.063
GPT teacher head0.436
Teacher spread0.373 · 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 designQualitative
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

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Military Veteran and Family HealthSame topicLGBTQ Health, Identity, and PolicyFrench-language works237,207