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Record W2963295179 · doi:10.1177/1059840519863094

First- and Second-Hand Experiences of Enacted Stigma Among LGBTQ Youth

2019· article· en· W2963295179 on OpenAlexaffabout
Amy L. Gower, Cheryl Ann B. Valdez, Ryan J. Watson, Marla E. Eisenberg, Christopher J. Mehus, Elizabeth Saewyc, Heather L. Corliss, Richard Sullivan, Carolyn M. Porta

Bibliographic record

VenueThe Journal of School Nursing · 2019
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of British Columbia
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute on Drug Abuse
KeywordsStigma (botany)PsychologyQueerGender studiesSociologyPsychiatry

Abstract

fetched live from OpenAlex

Research on enacted stigma, or stigma- and bias-based victimization, including bullying and harassment, among lesbian, gay, bisexual, transgender, and queer (LGBTQ) youth often focuses on one context (e.g., school) or one form (e.g., bullying or microaggressions), which limits our understanding of these experiences. We conducted qualitative go-along interviews with 66 LGBTQ adolescents (14-19 years) in urban, suburban, town, and rural locations in the United States and Canada identified through purposive and snowball sampling. Forty-six participants (70%) described at least one instance of enacted stigma. Three primary themes emerged: (1) enacted stigma occurred in many contexts; (2) enacted stigma restricted movement; and (3) second-hand accounts of enacted stigma shaped perceptions of safety. Efforts to improve well-being among LGBTQ youth must address the diverse forms and contexts of enacted stigma that youth experience, which limit freedom of movement and potential access to opportunities that encourage positive youth development. School nurses can play a critical role in reducing enacted stigma in schools and in collaboration with community partners.

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 score0.495
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.336
Teacher spread0.305 · 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

Citations43
Published2019
Admission routes2
Has abstractyes

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