Spaces of convergence in a cancer clinical genomics trial: a survey examining genomic literacy among medical oncologists in British Columbia
Bibliographic record
Abstract
The emergence of big data in the network age has led to many innovative breakthroughs in all sectors of life.One significant breakthrough are the prominent applications of clinical genomics in developing personalized medicine.In this thesis I explore the technological diffusion of clinical genomics within the spaces of convergence of multidisciplinary medical stakeholders in the Personalized Onco-Genomics (POG) cancer clinical trial.I co-developed the concept of "Genomic literacy" by drawing upon three areas of scholarship: health communication, information communication technologies (ICTs), and science and technology.I gathered data using a survey and semi-structured interviews with medical oncologists and other scientists at POG.Using this data I examine how genomic literacy, attitudes, and experiences of the domain experts working with clinical genomics can determine the adoption of genomic technologies into clinical care.These spaces of convergence of multidisciplinary medical stakeholders also create a pedagogical space where the stakeholders come together.This bioclinical collective of stakeholders learn more about genomics through their communicative and discursive processes, as they co-construct knowledge and meaning with genomic information.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".