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Record W7061197155

Pre-Surgical Mental Health Readiness Assessments for Gender Affirming Surgeries: A Survey of Canadian Practices

2020· other· en· W7061197155 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2020
Typeother
Languageen
FieldEngineering
TopicSuperconducting Materials and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthReferralTransgenderMental health careHealth careMEDLINEInformed consentPreference
DOInot available

Abstract

fetched live from OpenAlex

Practitioners providing pre-surgical assessments for gender affirming surgery in Canada (N=70) were surveyed on their assessment procedures, their interpretations of the World Professional Association for Transgender Health Standards of Care (SOC7) guidelines, and perspectives on future referral strategies. Most practitioners identified the SOC7 as their main or only source of pre-surgical assessment guidelines, although many other guidelines were cited as key resources. A significant proportion of practitioners indicated they used personally developed guidelines. Practitioners demonstrated a preference for decision making models other than the SOC7, the most popular being a direct referral from a primary care provider with the option of a mental health consult in the case of complex mental health presentations. Most practitioners approved of the trend towards an informed consent model for accessing gender affirming surgery, though the importance of mental health experts™ involvement was recognized. This cross-sectional survey demonstrates the importance of updated WPATH standards of care, and the development and adherence to a standardized evidence-based approach to pre-surgical mental health assessment for gender affirming surgery across Canada.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.082
GPT teacher head0.285
Teacher spread0.203 · 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.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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