Patient outcomes of genetic counseling: Assessing the impact of different approaches to family history collection
Bibliographic record
Abstract
No studies have yet evaluated whether different modalities for the collection of family history data influence patient outcomes of genetic counseling. We retrospectively compared outcomes of genetic counseling between patients whose family history (Fhx) was collected (1) via telephone prior to their appointment (FhxPrior) or (2) during the appointment (FhxDuring). We used a psychiatric genetic counseling clinic database, where information about demographics and Fhx timing is recorded, and patients complete the Genetic Counseling Outcomes Scale (GCOS, measuring empowerment) and Illness Management Self-Efficacy Scale (IMSES) immediately prior to (T1) and 1 month after their appointment (T2). We used ANCOVA to evaluate the effect of the Fhx method on patient outcomes at T2. Complete data were available for 240 patients and were used for analysis (FhxPrior, n = 206; FhxDuring, n = 34). GCOS and IMSES scores increased from T1 to T2 (P < .0005 and P = .004, respectively). Although there was no difference between groups for GCOS (P = .412), T2 IMSES scores were significantly higher for FhxPrior than FhxDuring after controlling for T1 scores (P = .011). Our data suggest that obtaining Fhx via telephone prior to genetic counseling may lead to greater increases in patients' self-efficacy as compared to obtaining Fhx during the genetic counseling appointment.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".