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Record W3007631573 · doi:10.1177/0840470419883661

Planning an intersectoral network of healthcare and community leaders to advance trans-affirming care for sexual assault survivors

2020· article· en· W3007631573 on OpenAlexafffundabout
Megan Saad, Joseph Friedman Burley, Melissa Miljanovski, Sheila Macdonald, Chett Bradley, Janice Du Mont

Bibliographic record

VenueHealthcare Management Forum · 2020
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsOntario HIV Treatment NetworkEtobicoke General HospitalPublic Health OntarioUniversity of TorontoWomen's College Hospital
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsChampionSexual assaultTransgenderHealth careEquity (law)Sexual violencePublic relationsPoison controlBusinessPolitical scienceSuicide preventionMedicineNursingSociologyMedical emergencyGender studies

Abstract

fetched live from OpenAlex

Sexual assault against transgender (trans) persons is a complex public health issue requiring the coordinated effort of multiple sectors to address. In response to a global call to improve health equity for persons of diverse gender identities, leaders across health and social service sectors need to enhance collaboration to champion trans-affirming care for sexual assault survivors. In collaboration with Egale Canada Human Rights Trust and the Ontario Network of Sexual Assault/Domestic Violence Treatment Centres, we have undertaken the development of an intersectoral network to connect trans-positive community organizations with hospital-based violence treatment centres to improve support services for trans survivors across Ontario. Guided by the Lifecycle Model for network development outlined by the National Collaborating Centre for Methods and Tools, we describe our approach to planning the intersectoral network, including key insights learned thus far and the potential of the network moving forward.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.107
GPT teacher head0.417
Teacher spread0.309 · 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 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

Citations9
Published2020
Admission routes3
Has abstractyes

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