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Record W2966505282 · doi:10.1177/1074840719864099

It’s Complicated: Improving Undergraduate Nursing Students’ Understanding Family and Care of LGBTQ Older Adults

2019· article· en· W2966505282 on OpenAlexafffund
Nadine Henriquez, Kathryn Hyndman, Kathryn Chachula

Bibliographic record

VenueJournal of Family Nursing · 2019
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsBrandon University
FundersRegistered Nurses' Association of OntarioUniversity of Manitoba
KeywordsTransgenderLesbianIntersectionalityQueerContext (archaeology)PsychologyCultural competenceHealth careCompetence (human resources)NursingMedicineGender studiesSociologySocial psychologyPedagogyPolitical science

Abstract

fetched live from OpenAlex

Research has identified the need for improved cultural competence of health care providers regarding the lesbian, gay, bisexual, transgender, and queer (LGBTQ) community's needs. This article articulates the teaching approach and methodology of an unfolding LGBTQ family case study for undergraduate nursing students. This method provided a forum for exploration of personal biases and gender-affirming techniques, and addressed the challenges of aging for a transgender woman and family within the context of societal stigma and discrimination. Students gained knowledge concerning shifts in family structures and understanding of the nurses' role encouraging inclusiveness and equitable access in health care settings, advocating for vulnerable populations, and addressing specific health concerns for transgender older adults. Student responses demonstrated increased knowledge of family diversity, and critical thought regarding the intersectionality of discrimination and aging. The findings revealed the case study methodology facilitated student understanding of the unique health and social issues for LGBTQ older adults within a family context.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.058
GPT teacher head0.387
Teacher spread0.329 · 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.

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

Citations32
Published2019
Admission routes2
Has abstractyes

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