Analysis of the Political Viewpoint of Policy Statements From Professional Medical Organizations Using ChatGPT With GPT-4: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Professional medical organizations publish policy statements that are used to impact legislation or address societal issues. Many organizations are nonpartisan, yet it is uncertain whether their policy statements balance liberal and conservative values. OBJECTIVE: This study aims to evaluate the political viewpoint of policy statements from 6 influential medical organizations, including the American Academy of Pediatrics, American College of Surgeons, American Psychiatric Association, American College of Obstetricians and Gynecologists, American College of Physicians, and American Academy of Family Physicians. METHODS: Between December 2023 and February 2024, policy statements from the 6 organizations were identified and evaluated using ChatGPT with GPT-4 to reduce bias. Each statement was pasted into a new ChatGPT session following the phrase "Does this text align with a liberal or conservative viewpoint?" Two authors reviewed each response and categorized the statement as liberal, probably liberal, neutral, probably conservative, or conservative. RESULTS: One-third of policy statements (529/1592, 33.2%) were found to be aligned with a political ideology. Among these 529 statements, 516 (97.5%) were liberal or probably liberal and 13 (2.5%) were conservative or probably conservative. For each organization, among policy statements with a political leaning, the percentage of liberal or probably liberal statements was as follows: 100% (69/69) for the American Academy of Pediatrics, 100% (24/24) for the American College of Obstetricians and Gynecologists, 100% (12/12) for the American College of Surgeons, 99% (72/73) for the American Psychiatric Association, 97% (174/180) for the American Academy of Family Physicians, and 96% (165/171) for the American College of Physicians. CONCLUSIONS: One in 3 policy statements from these 6 professional organizations align with a partisan political viewpoint. Among these, positions are 40 times more likely to be liberal or probably liberal than conservative or probably conservative. Whether or not organizations are politically neutral and seek viewpoint diversity warrants further exploration.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".