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Record W4295941050 · doi:10.9771/cgd.v8i2.49125

Health policies for the LGBT population, cultural competence, and the organization for access to services:

2022· article· en· W4295941050 on OpenAlexaboutno aff
Camila Amaral Moreno Freitas, Vinícius Nunes Carvalho, Náila Neves de Jesus, Marcos Vinícius da Rocha Bezerra, Adriano Maia dos Santos, Clávdia Nicolaevna Kochergin, Nília Maria de Brito Lima Prado

Bibliographic record

VenueCadernos de Gênero e Diversidade · 2022
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsPublic healthHealth policyHealth promotionScope (computer science)Health carePopulationReproductive healthDiversity (politics)Health equity

Abstract

fetched live from OpenAlex

This is a literature review that aimed to analyze the scope of public health policies for the LGBT population in different countries on the European continent, North America and Oceania in order to identify the differences and similarities in content and organization of services and programs. 24 articles were selected to compose the corpus of this review. The results demonstrated the existence of different scopes of health policies for LGBT in the USA, Canada, Australia and the United Kingdom and a heterogeneous result with regard to the objective of the implementation of actions, with emphasis on directions related to LGBT aging, smoking cessation, control of alcohol and other drugs use, as well as cancer and HIV care policies. There was a predominance of approaches limited to the diseases (or unsafe sexual practices, instead focus to comprehensive health care to the LGBT population at different levels of complexity in health care. Furthermore, cultural differences imply granting comprehensive or restrictive health rights. It's necessary to improve the design of public health promotion policies for gender and sexual diversity that are more inclusive and concatenated with other determinants that permeate comprehensive health care.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.040
GPT teacher head0.374
Teacher spread0.334 · 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.

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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueCadernos de Gênero e DiversidadeSame topicLGBTQ Health, Identity, and PolicyFrench-language works237,207