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Record W2791095283 · doi:10.3148/cjdpr-2018-001

Towards Providing Culturally Aware Nutritional Care for Transgender People: Key Issues and Considerations

2018· article· en· W2791095283 on OpenAlexaffvenue
Pamela Fergusson, Nicole Greenspan, Lukas Maitland, Rémy Huberdeau

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2018
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsManitoba Beekeepers' AssociationSt. Michael's HospitalToronto Metropolitan University
Fundersnot available
KeywordsTransgenderReferralStigma (botany)MedicineHealth careCultural competenceNursingSocial stigmaPsychologyMedical educationFamily medicineGerontologyHuman immunodeficiency virus (HIV)Psychiatry

Abstract

fetched live from OpenAlex

Transgender people are an important group for whom access to healthcare is often problematic. Dietitians need to be aware of key issues in transgender health to provide culturally competent clinical nutritional care. This article serves as a primer, clarifying key terms and concepts, exploring the impact of stigma and discrimination on health and nutrition for people from transgender communities, and offering practical advice for nutritional and other related issues. Education for dietitians both pre- and postqualification is an important part of improving care and building skills and awareness of cultural humility. Transgender people may be at increased nutritional risk due to increased risk of cardiovascular disease, HIV, body image issues, and food insecurity. This risk profile, along with the history of trauma both outside and related to the medical community means that there is an urgent need for dietitians to develop practice tools for assessment, care, and referral to improve the nutritional status and well-being of this client group.

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)0.002
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: none
Teacher disagreement score0.578
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.157
GPT teacher head0.494
Teacher spread0.337 · 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

Citations37
Published2018
Admission routes2
Has abstractyes

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