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Record W4399679880 · doi:10.1002/jgc4.1909

Identifying potential <scp>LGBTQIA</scp><sup>+</sup> competencies for genetic counseling student training

2024· article· en· W4399679880 on OpenAlexaboutno aff
Grace Saunders, Erin P. Carmany, Angela Trepanier

Bibliographic record

VenueJournal of Genetic Counseling · 2024
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsFocus groupAccreditationHealth careMedical educationThematic analysisMedicinePsychologyQualitative researchSociology

Abstract

fetched live from OpenAlex

Abstract The LGBTQIA + community faces considerable health disparities. Developing and integrating LGBTQIA + competencies into healthcare provider training programs is one way to promote inclusive high‐quality care to potentially improve this community's health. Currently, there are no established LGBTQIA + ‐specific competencies for genetic counseling graduate programs (GCPs), so training across GCPs likely varies. This qualitative focus group‐based study aimed to explore current topics related to genetic counseling (GC) for LGBTQIA + patients covered in North American GCPs, their learning objectives, and LGBTQIA + ‐specific competencies that GC students (GCSs) should achieve by graduation. Eligible participants were program leaders at ACGC‐accredited GCPs and/or faculty who taught LGBTQIA + ‐related content in at least one GCP over the last 5 years. A semistructured interview guide was used to conduct virtual focus groups that were recorded and transcribed. Transcripts were analyzed using reflexive thematic analysis and an inductive iterative approach that generated themes regarding what content is taught and what knowledge, attitudes, and skills GCSs should demonstrate as a result. Thirteen people participated, including nine LGBTQIA + people. They represented 12 GCPs (22% of current GCPs) across the United States and Canada. Focus groups ran 73–90 min. Transcript analysis identified six themes, framed as learning objectives (LOs), and 24 subobjectives. These included recognizing the breadth of the LGBTQIA + community and their lived experience with the healthcare system, demonstrating respect for and responding to patient identities to provide inclusive GC, employing strategies to mitigate social aspects that influence health care, and assessing personal biases and the impact of socialization. Participants used several teaching methods including didactic lectures, simulation, written activities, reflections, and fieldwork experiences. The six identified LOs may be a starting point for GCPs looking to develop or refine their LGBTQIA + curricula. Competency‐based education may enhance GCSs' abilities to provide inclusive GC to the LGBTQIA + community.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.048
GPT teacher head0.361
Teacher spread0.313 · 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 designQualitative
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

Citations1
Published2024
Admission routes1
Has abstractyes

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