Genetic counselors' perceptions of uncertainty in pretest counseling for genomic sequencing: A qualitative study
Bibliographic record
Abstract
Increased usage of exome and genome sequencing has made uncertainties associated with genomic sequencing methods more prevalent within medicine. Current research focuses on patients' perceptions of uncertainty related to genomic sequencing, but there is limited knowledge of the perspectives of providers. The aim of this study was to explore how professionals in genomics perceive uncertainties involved in genomic sequencing, and if or how this impacts their approach to pretest counseling. We performed 20 semi-structured interviews with genetic counselors in the United States and Canada who provide pretest genetic counseling for genomic sequencing. Interviews explored participating genetic counselors' views of uncertainty regarding genomic sequencing, how they classify it, how it manifests, and how they manage it during pretest counseling. Thematic analysis showed that genetic counselors acknowledge concepts of uncertainty that map to existing frameworks of uncertainty for genomic sequencing. Genetic counselors also perceived incongruencies between patients' and providers' expectations of genomic sequencing, which prompted them to modify patients' perceptions of uncertainty related to genomic sequencing. All genetic counselors agreed that guidance and strategies for genomic sequencing pretest counseling would be helpful, particularly for novice genetic counselors and non-genetics providers. These findings highlight the need and potential for conceptual models of uncertainty and uncertainty management strategies to facilitate patient-centered pretest counseling for genomic sequencing.
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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".