Exploring genetic counselors' experiences with non‐paternity in clinical settings
Bibliographic record
Abstract
Non-paternity (NP) is a challenging dilemma faced by genetics providers and there is little consensus on whether this finding should be disclosed. Discussions in the literature are highly theoretical, with limited research regarding how disclosure decisions are enacted in practice. We explored genetic counselors' (GCs) clinical experiences with NP to understand if, how, and why this finding is communicated. Our semi-structured interviews with genetic counselors in the United States and Canada were analyzed using reflexive thematic analysis to analyze data inductively, describe themes, and present a meaningful interpretation of the data. Eighteen participants who responded to list-serv messages were interviewed. Our framework describes five salient themes: (1) GC-lab relationship: the GCs awareness of laboratory processes such as quality control metrics that can uncover NP findings and the way in which a finding of NP was disclosed by the laboratory had an impact on disclosure decisions. This triggered a decision-making trajectory that involved (2) consultation, (3) ethical reasoning, and (4) practical constraints. GCs frequently consulted other professionals during decision-making. These conversations impacted disclosure decisions with some consultations carrying greater weight than others. GCs weighed moral concepts of patient autonomy, medical relevance, and preventing harm to rationalize decisions. Access to patients and documentation requirements often dictated how disclosure occurred. Finally, once a decision had been made and enacted, GCs used the experience to reconsider their approach to (5) consenting in future cases, with some GCs altering their pre-test counseling to always include a discussion of NP. Although NP scenarios are frequently unique in context, our findings demonstrate several common decision-making factors GCs harness to navigate the identification of NP through clinical genetic testing.
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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 | 0.023 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".