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Record W3177107784 · doi:10.1089/trgh.2020.0184

Development of a WebPortal to Advance and Mobilize Knowledge Relevant to Trans-Affirming Care for Sexual Assault Survivors in Ontario, Canada

2021· article· en· W3177107784 on OpenAlexaffabout
Janice Du Mont, Sarah Daisy Kosa, Joseph Friedman Burley, Sheila Macdonald

Bibliographic record

VenueTransgender Health · 2021
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsOntario HIV Treatment NetworkPublic Health OntarioWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsTransgenderSexual assaultHealth careService providerNursingMedicineService (business)PsychologyPublic relationsPolitical scienceSuicide preventionPoison controlBusinessMedical emergency

Abstract

fetched live from OpenAlex

Transgender persons experience high rates of sexual assault and often encounter providers who lack the knowledge to deliver appropriate postassault care and supports. To build capacity within health care and social service organizations supporting trans survivors of sexual assault across Ontario, Canada, we undertook a study to inform the development of a WebPortal intended to provide freely accessible resources relevant to the provision of trans-affirming care. In this survey, 70 representatives from community and health care organizations indicated their overall dissatisfaction with the information currently available on the care and support of trans survivors and identified a need for improved access to a range of resources.

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)0.009
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.973

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.058
GPT teacher head0.374
Teacher spread0.317 · 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 designNot applicable
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

Citations4
Published2021
Admission routes2
Has abstractyes

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