Interactive e‐counselling for genetics pre‐test decisions: where are we now?
Bibliographic record
Abstract
In-person genetic counselling (GC) is the model typically used to provide patients with information regarding their genetic testing options. Current and emerging demand for genetic testing may overburden the health care system and exceed the available numbers of genetic counsellors. Furthermore, GC is not always available at times and places convenient for patients. There is little evidence that the in-person model alone is always optimal and alternatives to in-person GC have been studied in genetics and other areas of health care. This review summarizes the published evidence between 1994 and March 2014 for interactive e-learning and decisional support e-tools that could be used in pre-test GC. A total of 21 papers from 15 heterogeneous studies of interactive e-learning tools, with or without decision aids, were reviewed. Study populations, designs, and outcomes varied widely but most used an e-tool as an adjunct to conventional GC. Knowledge acquisition and decisional comfort were achieved and the e-tools were generally well-accepted by users. In a time when health care budgets are constrained and availability of GC is limited, research is needed to determine the specific circumstances in which e-tools might replace or supplement some of the functions of genetic counsellors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".