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Record W3176329602 · doi:10.1089/trgh.2020.0087

Evaluation of an e-Learning Curriculum for Forensic Nurses on Trans-Affirming Postsexual Assault Care

2021· article· en· W3176329602 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueTransgender Health · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsOntario HIV Treatment NetworkPublic Health OntarioWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsCurriculumCompetence (human resources)ReferralForensic scienceMedical educationSexual assaultForensic nursingForensic examinationPsychologyMedicineNursingSuicide preventionPoison controlPedagogyMedical emergencyEngineeringSocial psychology

Abstract

fetched live from OpenAlex

Trans survivors of sexual assault have called for the development and implementation of training for care providers. To answer this call, we developed and evaluated an innovative e-learning curriculum for forensic nurses working across Ontario, Canada, on the provision of trans-affirming care. The e-learning curriculum, developed in Storyline 360 by Articulate, was launched in August 2019. The competence of nurses ( N =65) completing the curriculum improved significantly from pre- to post-training across all content domains (Initial assessment, Medical care, Forensic examination, and Discharge and referral). This e-learning curriculum could be of utility in training forensic nurses worldwide.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.458
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.127
GPT teacher head0.455
Teacher spread0.328 · 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