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Record W4405099390 · doi:10.22215/etd/2024-16318

Trans Autistic Experiences of Gender-Affirming Care in Ontario

2024· dissertation· en· W4405099390 on OpenAlexafffundabout
Kai Violet Gabrielle Jacobsen

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsCarleton University
FundersCouncil of Ontario Universities
KeywordsDistrustMedicalizationAutonomyHealth careQualitative researchAutismInjusticeResistance (ecology)PsychologyEmbodied cognitionNursingMedicineDevelopmental psychologySocial psychologyPsychiatrySociologyPsychotherapistPolitical science

Abstract

fetched live from OpenAlex

This research focused on trans autistic people's experiences of gender-affirming health care, such as hormones and surgeries.I conducted qualitative semi-structured interviews with 12 autistic adults who had accessed gender-affirming care in Ontario, Canada in the past five years.My findings reveal how the overarching discourses of medicalization, transnormativity, neuronormativity, and medical authority structure clinical protocols and guidelines as well as patient-provider interaction.Consequently, trans autistic people frequently experience epistemic injustice, distrust, barriers to care, and other harms in health care.However, participants also described positive health care experiences and strategies of resistance.Participants envisioned how health care could support their selfdetermination by respecting their autonomy and embodied knowledge.The results highlight how healthcare policy and practice can be improved for trans autistic people as well as theoretical implications for trans studies and critical autism studies.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.116
Threshold uncertainty score0.308

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0260.017
Scholarly communication0.0040.002
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.336
Teacher spread0.287 · 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

Citations1
Published2024
Admission routes3
Has abstractyes

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