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Record W4412464841 · doi:10.1016/j.jcpo.2025.100621

Turning lessons into action: Building an inclusive oncology care for transgender and gender-diverse people- Pavia & Milano framework

2025· article· en· W4412464841 on OpenAlexaff
Amelia Barcellini, Chiara Cassani, Anna Maria Mancuso, Claudio Baggini, Laura Beduschi, Elisabetta Bettega, Fabiola Bologna, Daniele Calzavara, Cristina Campiglio, Francesco Celestina, Maria Grazia Colombo, Claudia Roberta Combei, Simone D'Alpaos, Silvia Deandrea, Silvia Desigis, Alessandra Dell’Era, Francesca Dionigi, Cinzia Fasola, Silvia Illari, Bianca Iula, Nicla La Verde, Manuela Nebuloni, Lorenzo Ruggieri, Raffaela D. G. Sartori, Simona Secondino, Maria Spinelli, Barbara Tagliaferri, Davide Dalu, Laura D. Locati

Bibliographic record

VenueJournal of Cancer Policy · 2025
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity Hospital Foundation
FundersGilead Sciences
KeywordsTransgenderAction (physics)Transgender peopleMedicineGerontologyPolitical scienceSociologyGender studies

Abstract

fetched live from OpenAlex

Italy remains one of the lowest-ranking Western European countries in terms of rights and protections for transgender and gender-diverse (TGD) individuals, with serious consequences for equitable healthcare access. In response to these disparities, and thanks to the initiative and support of the advocacy group Salute Donna Odv Salute Uomo, a multidisciplinary team organised a series of public conferences in Pavia and Milan in 2024. These events brought together healthcare professionals, researchers, LGBTQIA+ organisations, and patient advocates to define practical strategies for more inclusive cancer care. The outcome is a shared framework presented in this manuscript, aimed at enhancing cultural competence and institutional responsiveness in oncology services for TGD patients.

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)0.029
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.201

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0170.047
Scholarly communication0.0200.009
Open science0.0040.035
Research integrity0.0080.016
Insufficient payload (model declined to judge)0.0050.001

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 head0.103
GPT teacher head0.546
Teacher spread0.443 · 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 designTheoretical or conceptual
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

Citations1
Published2025
Admission routes1
Has abstractyes

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Same venueJournal of Cancer PolicySame topicLGBTQ Health, Identity, and PolicyFrench-language works237,207