Interactive e‐counselling for genetics pre‐test decisions: where are we now?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it