Representing a “revolution”: how the popular press has portrayed personalized medicine
Bibliographic record
Abstract
PURPOSE: This study investigated the portrayal of "personalized" and "precision" medicine (PM) in North American news over the past decade. Content analysis of print and online news was conducted to determine how PM has been defined and to identify the frames used to discuss PM, including associated topics, benefits, and concerns. METHODS: A data set was built using the FACTIVA database, searching for popular North American publications with the terms "personalized (personalised) medicine" and/or "precision medicine" from 1 January 2005 to 15 March 2016. The final set of publications totaled 774. RESULTS: PM is almost exclusively defined as related to genetics and is often part of a story related to cancer. The PM story is overwhelmingly one of highlighting (potential) benefits and optimism, especially in shorter publications, and ones where PM is not the main focus. This promotional PM discourse has remained fairly consistent over the past decade. CONCLUSION: The numerous concerns associated with PM have received little attention over the past decade, especially in articles more likely to be encountered by a more general audience. This promotion of PM serves as an example of the science hyping that takes place in science reportage and may have implications for consumers, public expectations, and related health policy.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".