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Record W2898359258 · doi:10.28984/drhj.v2i0.132

The Health Needs and Experiences of Trans Residents in Small and Rural Areas

2018· article· en· W2898359258 on OpenAlexaffvenue
Tanya Shute

Bibliographic record

VenueDiversity of Research in Health Journal · 2018
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsLaurentian University
Fundersnot available
KeywordsTransgenderHealth careDenialFocus groupRural areaNursingQualitative researchMedicinePsychologyGerontologySociologyPolitical science

Abstract

fetched live from OpenAlex

This brief paper summarizes the findings from a community-based research project examining the health needs and experiences of trans-identified people in small and rural communities as presented at the 9th annual Laurentian University Faculty of Health conference. This study involved residents who identify as transgender living in North Simcoe/Muskoka, an area comprised of small, rural, suburban and remote communities. It employed a mixed method design, with quantitative findings derived from a comprehensive online survey and qualitative findings from a series of community focus groups. A sample of findings related to health care experiences grounded in the voices of participants was presented. These findings included several common themes that characterize the health service encounter of residents who are transgender. The health care experience of trading off competent trans-specific health care provision for respect and willingness on behalf of the health care practitioner was common, and provides evidence for the lack of trans-specific health care available in these areas. Experiences of service denial or rejection as a result of their trans identities or gender expression were also common. Residents who are transgendered in areas where there is a lack of service infrastructure are also forced to become their own health care experts, a necessary and distressing reality of accessing health care as a transgender individual in small and rural areas.

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)0.008
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.203
GPT teacher head0.491
Teacher spread0.288 · 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

Citations2
Published2018
Admission routes2
Has abstractyes

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