How Might the Genetics Profession Better Utilize Social Media
Bibliographic record
Abstract
Social media is a common method of communication in people's personal lives and professional settings. Gallagher et al. (2016) recommended, "it is time for genetic counselors to embrace social media as a means of communicating with patients or other healthcare professionals." Full members of the National Society of Genetic Counselors (NSGC) in the USA and Canada and genetics patients in Cleveland, OH, were surveyed to determine interest in using social media for patient-provider interactions. Both cohorts indicated that patient privacy and confidentiality would be a concern; however, survey results indicated patients would be interested in using social media to receive general information about genetic counseling and to learn about genetics services. Genetic counselors indicated privacy issues were not concerning if social media were to be used in this capacity. The majority of genetic counselor participants (88.7%) indicated they would welcome national guidelines for patient-provider social media use. Data from this study demonstrated that sharing what to expect at a genetic counseling appointment, defining genetic counseling, and announcing community outreach events are possible ways genetic counselors could utilize social media to communicate with and educate patients.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".