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Record W1997461925 · doi:10.1080/15532739.2011.663243

Depression in Trans People: A Review of the Risk Factors

2012· review· en· W1997461925 on OpenAlexaff
Nooshin Khobzi Rotondi

Bibliographic record

VenueInternational Journal of Transgenderism · 2012
Typereview
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsTransgenderTranssexualSocioeconomic statusDepression (economics)Mental healthPsychologyMoodPsychiatryClinical psychologyMedicineEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

Depression is a commonly occurring syndrome characterized by mood-related symptoms; however, it is understudied among trans people (transgender, transsexual, or transitioned). A review of the existing literature on depression revealed eight factors pertinent to trans communities including discrimination, disclosure, identity support, hormones and sex-reassignment surgeries, sociodemographics, socioeconomic factors, substance use, and access to health and social services. This report demonstrates that depression in trans people is a multifaceted condition, which is not easily explained by a single factor. Developing an understanding of such complexities may open the door to more sensitive and appropriate mental health care for trans people.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.111
GPT teacher head0.451
Teacher spread0.340 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations37
Published2012
Admission routes1
Has abstractyes

Explore more

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