Coffee and conversation: A genuine dialogue on authentic professional learning between genetic counselor educators
Bibliographic record
Abstract
Lifelong learning is a term frequently referred to in the training and continuing professional development of genetic counselors. It implies the ability to continuously engage in self-motivated reflection to identify knowledge gaps and develop a learning plan to address identified needs or interests. In contrast to this definition, the path to continuing professional development for most genetic counselors involves attendance at conferences; yet much data suggest that other forms of learning are more effective at leading to practice change and improved patient or quality outcomes. These conflicting ideas beg the question: what is professional learning? A dialogue between two genetic counselor educators, both with advanced training in health professional education, shares personal beliefs regarding lifelong learning in the genetic counseling profession. This discourse represents an authentic conversation that was audio-recorded and transcribed with minimal editing to improve clarity and readability. The views presented in this dialogue are highly personal, yet grounded in educational theory. References are provided to those that desire further reading on the topics discussed. Several authentic learning strategies are described, including communities of practice, peer supervision, and personal learning projects. The authors consider ways to increase knowledge acquisition from conference attendance and discuss how learning on the job becomes embedded in practice. As a result of this discourse, the authors hope to inspire genetic counselors to reflect over their continuing professional development and consider their job as a learning environment that presents rich, ongoing, and unique opportunities for growth. The authors invite and challenge readers to identify learning needs and set goals for themselves to address those needs. For those with interest in education, it is hoped that the conversation sparks new or invigorated interest that will lead to novel or more effective learning opportunities with improved outcomes for patients, students, and colleagues alike.
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 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 | 0.021 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.036 | 0.040 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".