“I <i>am</i> a Genetic Counselor”: A qualitative exploration of field leaders’ perceptions of the title “genetic counselor”
Bibliographic record
Abstract
The genetic counseling profession is 50 years old, and is growing and diversifying. Despite this evolving context, no studies have formally explored the continuing relevance or appropriateness of the title "genetic counselor." We used a qualitative research methodology (interpretive description) to explore this concept among thought leaders within the genetic counseling profession. We conducted 12 semi-structured telephone interviews, which ranged in length from 18 to 50 min, and transcribed them verbatim. Analysis and data collection unfolded in parallel. The following themes regarding participants' perceptions of the title "genetic counselor" emerged from the data: (1) others misunderstand "genetic counselor"; (2) the term "counselor" in our title produces complex and conflicting emotions; (3) risks of changing our title outweigh the benefit; and (4) we need to own the narrative surrounding our title. Despite recognition that the title "genetic counselor" may not capture the full range of diverse roles members of the profession play, our data reveal overall support for the continued relevance and appropriateness of the title, for the value of the strength that comes from unity within the profession, and for efforts to highlight that which unites us across roles, disciplines, and specialties.
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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 | 0.028 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".