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Record W2012724507 · doi:10.1007/s10897-013-9651-x

The Establishment of Core Competencies for Canadian Genetic Counsellors: Validation of Practice Based Competencies

2013· article· en· W2012724507 on OpenAlexaffabout
Raechel A. Ferrier, Mary Connolly‐Wilson, Jennifer Fitzpatrick, Sonya Grewal, Laura Robb, Julie Rutberg, Margaret Lilley

Bibliographic record

VenueJournal of Genetic Counseling · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsUniversity of OttawaMontreal Heart InstituteNorth York General HospitalAlberta Children's HospitalMcGill UniversityAlberta Health Services
FundersNational Society of Genetic Counselors
KeywordsCertificationCore competencyMedical educationGenetic counselingMedicineCurriculumHealth carePsychologyNursingPedagogyPolitical scienceBusiness

Abstract

fetched live from OpenAlex

Numerous groups of health professionals have undertaken the task of defining core competencies for their profession. The goal of establishing core competencies is to have a defined standard for such professional needs as practice guidelines, training curricula, certification, continuing competency and re-entry to practice. In 2006, the Canadian Association of Genetic Counsellors (CAGC) recognized the need for uniform practice standards for the profession in Canada, given the rapid progress of genetic knowledge and technologies, the expanding practice of genetic counsellors and the increasing demand for services. We report here the process by which the CAGC Practice Based Competencies were developed and then validated via two survey cycles, the first within the CAGC membership, and the second with feedback from external stakeholders. These competencies were formally approved in 2012 and describe the integrated skills, attitudes and judgment that genetic counsellors in Canada require in order to perform the services and duties that fall within the practice of the profession responsibly, safely, effectively and ethically.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score0.455

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 integrity0.0000.000
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 head0.015
GPT teacher head0.264
Teacher spread0.249 · 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 designBench or experimental
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

Citations23
Published2013
Admission routes2
Has abstractyes

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