News Media Representations of Responsibility for Alcohol-Related Liver Disease Requiring Liver Transplantation
Bibliographic record
Abstract
Alcohol-related liver disease (ARLD) is a common indication for liver transplantation yet it is considered ethically controversial in academic, clinical and public discourses. Various social groups consider people with ARLD as personally responsible for their condition and question whether they should have access to a scarce resource. How the news media constructs responsibility for ARLD may influence public opinions toward those who are ill as well as related healthcare policies. Since the organ transplantation system relies on the willingness of individuals to donate organs, understanding how the media portrays controversial issues is a matter of vital importance for public health and health policy. We investigated how responsibility for ARLD requiring liver transplantation is presented for public consumption in the news media. Using a keyword search of two online news databases, we selected 81 articles from the United Kingdom, Canada and the United States. We analyzed the articles using a discursive psychological approach. We found that the news media ascribed responsibility for ARLD to three main actors: individuals with ARLD, biological predisposition, and policy and industry representatives. How responsibility for ARLD requiring liver transplantation is presented in the news media may have implications for people diagnosed with other substance-related disorders who present for transplant candidacy or are on the transplant waiting list. Investigating how responsibility for ARLD is constructed in news media may provide insights into how responsibility is understood in other stigmatized health conditions and its potential implications for population health equity.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".