2013 Review and Update of the Genetic Counseling Practice Based Competencies by a Task Force of the Accreditation Council for Genetic Counseling
Bibliographic record
Abstract
The first practice based competencies (PBCs) for the field of genetic counseling were adopted by the American Board of Genetic Counseling (ABGC), 1996. Since that time, there has been significant growth in established and new work settings (clinical and non-clinical) and changes in service delivery models and the roles of genetic counselors. These changes prompted the ABGC to appoint a PBC Task Force in 2011 to review the PBCs with respect to their current relevance and to revise and update them as necessary. There are four domains in the revised PBCs: (I) Genetics Expertise and Analysis (II) Interpersonal, Psychosocial and Counseling Skills (III) Education and (IV) Professional Development and Practice. There are 22 competencies, each clarified with learning objectives or samples of activities and skills; a glossary is included. New competencies were added that address genomics, genetic testing and genetic counselors' roles in risk assessment, education, supervision, conducting research and presenting research options to patients. With PBCs serving as the pre-defined abilities or outcomes of training, graduating genetic counselors will be well prepared to enter the field with a minimum level of skills and abilities. A description of the Task Force's work, key changes and the 2013 PBCs are presented herein.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".