MétaCan
Menu
Back to cohort
Record W4411893084 · doi:10.1002/jgc4.70070

Genetic counseling training program perspectives on delivering disability‐related education

2025· article· en· W4411893084 on OpenAlexaboutno aff
Anna R. Miller, Ashley Kuhl, Rachel Sullivan, Catherine A. Reiser, Elizabeth M. Petty

Bibliographic record

VenueJournal of Genetic Counseling · 2025
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumAccreditationGenetic counselingPreparednessMedical educationMedicineResource (disambiguation)Counselor educationPsychologyNursingHigher educationPedagogyPolitical science

Abstract

fetched live from OpenAlex

Ensuring genetic counselors are educated about disability is crucial due to the societal implications of genetic testing and the support they provide to clients. Research indicates that genetic counselor preparedness in these areas is both limited and variable, with practitioners and disability advocates expressing desire for more robust disability-related curricula. This study aimed to gain updated information and perspectives on relevant curricula currently offered at accredited genetic counseling training programs. With future goals of filling in curricula gaps, we also investigated program leadership interest in a shared disability-related curricula resource. Leadership from 16 out of 51 accredited genetic counseling training programs in the United States and Canada responded to our survey. We found that current disability-related curricula are often based in classroom didactics, which is more likely to be focused on medical aspects of disability, whereas community-based education is more likely to expose students to community support resources and lived experiences. As such, our study highlights the variability of genetic counselor training about disability and gaps in community-based education. To fill in these gaps, we found that all programs expressed interest in a curricula development resource.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.944
Threshold uncertainty score0.755

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.013
GPT teacher head0.303
Teacher spread0.290 · 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 designOther design
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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Genetic CounselingSame topicPrenatal Screening and DiagnosticsFrench-language works237,207