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Record W2784583325 · doi:10.1007/s10897-018-0215-y

How Might the Genetics Profession Better Utilize Social Media

2018· article· en· W2784583325 on OpenAlexaboutno aff
Rebekah Moore, Anne L. Matthews, Leslie Cohen

Bibliographic record

VenueJournal of Genetic Counseling · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsnot available
FundersEngelberg FoundationNational Society of Genetic Counselors
KeywordsGenetic counselingConfidentialityOutreachSocial mediaGenetic testingMedical geneticsPsychologyMedicineMedical educationPublic relationsFamily medicineGeneticsWorld Wide WebPolitical scienceComputer science

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.094
GPT teacher head0.389
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2018
Admission routes1
Has abstractyes

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