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Record W3179909018 · doi:10.3138/jmvfh-2021-0032

Mental health and well-being of LGBT+ Veterans dismissed from the British Armed Forces before January 2000

2021· article· en· W3179909018 on OpenAlexvenueno aff
Caroline Paige, Christina Dodds, Craig Jones

Bibliographic record

VenueJournal of Military Veteran and Family Health · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthPrideTransgenderLesbianGeneral partnershipSexual orientationPolitical scienceMilitary personnelPsychologyGender studiesCriminologySociologyLawPsychiatry

Abstract

fetched live from OpenAlex

LAY SUMMARY Between 1955 and January 2000, the UK Armed Forces and Ministry of Defence enforced a ban on lesbian, gay, bisexual, transgender, and related (LGBT+) service, dismissing or forcing the immediate retirement of thousands of personnel because of their sexual orientation or gender identity. They fell on hard times and were left isolated and unsupported by the nation they had proudly stood to defend. Although more than 21 years has elapsed since the ban was lifted, little academic literature has explored the ban’s impact on the mental health and well-being of the United Kingdom’s LGBT+ Veteran community. Anecdotal evidence suggests many still endure consequential hardship and mental health struggles and remain isolated from the military family and traditional support services. Fighting With Pride, an LGBT+ military charity launched in January 2020, and Northumbria University’s Veterans and Military Families Research Hub joined in partnership to remedy this by determining mental health and well-being impacts and consequences and identifying recovery pathways. Lived experience narratives must be used to help build support ahead of the publication of any formal findings. Research-based evidence is vital in helping to develop recovery and support policy and in further shaping support services to develop the best possible impact-related outcomes.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score1.000

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 head0.019
GPT teacher head0.292
Teacher spread0.273 · 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 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

Citations6
Published2021
Admission routes1
Has abstractyes

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