Assessing Shared Decision‐Making Clinical Behaviors Among Genetic Counsellors
Bibliographic record
Abstract
Abstract Shared decision‐making (SDM) is a collaborative approach in which clinicians educate, support, and guide patients as they make informed, value‐congruent decisions. SDM improves patients' health‐related outcomes through increasing knowledge, reducing decisional conflict, and enhancing experience of care. We measured SDM in genetic counselling appointments with 27 pregnant women who were at increased risk to have a baby with a genetic abnormality. The eight experienced genetic counsellors who participated had no specific SDM training and were unaware that SDM was being assessed. Audio transcripts of appointments were scored using ‘Observing Patient Involvement in Decision Making' (OPTION12). Patients' anxiety and decisional conflict were also assessed. The genetic counsellors' mean OPTION12score was 42.4% (SD 9.0%; possible range 0–100%). Specific SDM behaviours that scored highest included introducing the concept of equipoise and listing all options with their pros and cons. Behaviours that scored lowest included eliciting patients' preferred approach to receiving information and desired degree of involvement in decision‐making. Patients' levels of anxiety and decisional conflict were unassociated with genetic counsellors' OPTION12scores. Some SDM behaviours were better demonstrated in this prenatal genetic counselling study than others. Formal training of genetic counsellors in SDM may enhance use of this approach in their professional practice.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".