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Record W2144447030 · doi:10.5402/2012/161097

Gender, Work, and Health for Trans Health Providers: A Focus on Transmen

2012· article· en· W2144447030 on OpenAlexafffundabout
Judith A. MacDonnell, Alisa Grigorovich

Bibliographic record

VenueISRN Nursing · 2012
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsYork University
FundersYork University
KeywordsHealth carePsychological resilienceQualitative researchPsychologySocial determinants of healthSociologyHealth equityNursingSocial psychologyPublic healthMedicinePolitical scienceSocial science

Abstract

fetched live from OpenAlex

Well-documented health research points to trans people's vulnerability to health inequities that are linked to deeply embedded structural and social determinants of health. Gender and work, as social determinants of health for trans people, both shape and are shaped by multiple factors such as support networks, social environments, income and social status, shelter, and personal health practices. There is a gap in the nursing literature in regards to research on work and health for diverse trans people and a virtual silence on the particular issues of trans-identified health providers. This qualitative study used comparative life history methodology and purposeful sampling to examine links among work, career, and health for transmen who are health providers. Semistructured interviews were completed with four Canadian transmen involved in health care professional and/or practice contexts with diverse professions, age, work, and transitioning experiences. Critical gender analysis showed that unique and gender-related critical events and influences shape continuities and discontinuities in their careerlives. This strength-based approach foregrounds how resilience and growth emerged through participants' articulation with everyday gender dynamics. These findings have implications for nursing research, education, and practice that include an understanding of how trans providers "do transgender work" and supporting them in that process.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.836
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.161
GPT teacher head0.455
Teacher spread0.293 · 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 designObservational
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

Citations22
Published2012
Admission routes3
Has abstractyes

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