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Record W2469718155 · doi:10.32920/22726796.v1

Service provider views on issues and needs for lesbian, gay, bisexual, and transgender youth

2023· article· en· W2469718155 on OpenAlexaboutno aff
Emily van der Meulen, Robb Travers, Adrian Guţă, Sarah Flicker, June Larkin, Chase Lo, Sarah McCardell

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsTransgenderLesbianSexual orientationService providerAgency (philosophy)MulticulturalismFocus groupSexual minorityService (business)Gender studiesPsychologySociologyBusinessPedagogy

Abstract

fetched live from OpenAlex

Lesbian, gay, bisexual, and transgender (LGBT) youth require appropriate, effective, and accessible sexual health services. Sexual minority youth living in large urban, multicultural cities have a complex range of service needs. As part of the Toronto Teen Survey, focus groups were conducted with 80 service providers from 55 agencies in the Greater Toronto Area to elicit their input concerning the changing service needs of LGBT youth, their increasing complexity as a client group, and obstacles to working effectively with them. Issues that arose in the focus groups included addressing the needs of LGBT youth across a large city that includes suburban areas, the need to address the specific service needs of transgender youth, and the intersection of racial and ethno-cultural diversity with sexual orientation. Service provider recommendations focused on the need for improved education and training and policy change at the agency level.

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)0.008
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.225

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.353
GPT teacher head0.477
Teacher spread0.124 · 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

Citations18
Published2023
Admission routes1
Has abstractyes

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