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Record W4214495281 · doi:10.1108/dpm-06-2021-0196

Aging queer in a pandemic: intersectionalities and perceptions

2022· article· en· W4214495281 on OpenAlexaff
A.J. Faas, Simon Jarrar, Noémie Gonzalez Bautista

Bibliographic record

VenueDisaster Prevention and Management An International Journal · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPandemicContext (archaeology)GrassrootsOriginalityDistancingHealth carePublic relationsPsychologyPublic healthSociologyPolitical scienceMedicineNursingQualitative researchCoronavirus disease 2019 (COVID-19)GeographyPoliticsSocial science

Abstract

fetched live from OpenAlex

Purpose The purpose of this study is to highlight the experiences and issues of an overlooked demographic: older LGBTQ + adults in the US, in the context of the COVID-19 pandemic. This allows the authors to explore possible changes in policy and practice regarding the management of the pandemic with attention to elderly LGBTQ. Design/methodology/approach Building on the authors’ experience in disaster research and a study of older LGBTQ + adults in the San Francisco Bay Area, the authors analyze key trends in COVID-19 pandemic management while drawing lessons from the AIDS epidemic. Findings The authors have found that LGBTQ + people, especially older and transgender individuals, have unique experiences with hazards and public safety and healthcare professionals and organizations (e.g. heteronormative care, traumatic insensitivity, deprioritizing essential treatments as elective). Second, older LGBTQ + adults' perceptions of state responses to pandemics were heavily influenced by experiences with the HIV/AIDS pandemic. And third, experiences with the COVID-19 pandemic have important implications for preventing, responding to and recovering from future epidemics/pandemics. Originality/value The authors point to two parallel implications of this work. The first entails novel approaches to queering disaster prevention, response and recovery. And the second is to connect the management of the COVID-19 pandemic to the principles of harm reduction developed by grassroots organizations to suggest new ways to think about contagion and organize physical distancing, while still socializing to take care of people’s physical and mental health, especially the more marginalized like elderly LGBTQ + people.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.999

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)0.0020.000

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 head0.052
GPT teacher head0.433
Teacher spread0.380 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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