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Record W2018973366 · doi:10.1007/s10897-006-9082-z

Information Processing in the Context of Genetic Risk: Implications for Genetic‐Risk Communication

2007· review· en· W2018973366 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Genetic Counseling · 2007
Typereview
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsBrock UniversityUniversity of Ottawa
Fundersnot available
KeywordsRisk communicationContext (archaeology)Genetic counselingPublic healthHuman geneticsGenetic testingMedicineGeneticsRisk analysis (engineering)BiologyGenePathology

Abstract

fetched live from OpenAlex

Communicating genetic-risk information is fraught with difficulties, and there are no universally accepted guidelines for clinical practice. In this paper, we suggest that information-processing models may offer some guidance for the communication of genetic risk. The paper reviews selected literature from health and social psychology, including defensive reactions to threatening health information, the Extended Parallel Process Model (EPPM) and Self Affirmation Theory. Ultimately, it presents the Heuristic-Systematic Model (HSM) of information processing as a useful perspective from which to view genetic-risk communication. Through our review of this literature, we identify some of the variables found to influence the systematic or heuristic processing of risk information and note their relevance to genetic counseling contexts. We suggest that systematic information processing is conducive to informed decision-making, as well as improved understanding of risk information. Clinical practice implications derived from our review of these literatures are noted.

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 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)0.005
metaresearch head score (Gemma)0.000
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: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.069
GPT teacher head0.404
Teacher spread0.335 · 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