Ethics and disinformation on the campaign trail: psychiatry, the Goldwater Rule, and the 2024 United States presidential election
Bibliographic record
Abstract
Less than nine months until the 2024 US presidential election and another divisive contest between incumbent president, Joe Biden, and former president, Donald Trump, looks likely.Although disclosed medical records show no evidence of psychiatric symptoms, there has been widespread psychopathological supposition about these candidates, influenced by their age profiles and polarised political trends. [1][2]2][3] Continuing into 2024 and the Republican primaries, this discourse could be heightened by growing disinformation generated by digital technologies and artificial intelligence (AI). 4midst this complex and sensitive landscape, psychiatrists may face significant challenges, reigniting contentious debates about commentary on public figures and impinging upon the American Psychiatric Association's (APA) Goldwater Rule 5 ; this regulation prohibits APA member-psychiatrists from discussing the mental health of individuals without assessment or consent.Accordingly, interventions involving relevant stakeholders, including the APA, international psychiatric bodies, the media, and technology organisations, may help safeguard the reputation of psychiatry and elevate sociopolitical exchanges around mental health.The mental health of American presidential candidates has continually attracted scrutiny. 6During the 2016 contest, such discussions became increasingly animated when psychiatrists provoked ethical controversies through public views on Mr. Trump. 1,5,7Later, in the 2020 campaign, speculation abounded about Mr. Biden, as exemplified when Mr. Trump publicised his own results from the Montreal Cognitive Assessment (a screening tool for cognitive decline and dementia). 3hese narratives have intensified throughout Mr. Biden's presidency and may be resonating with diverse constituencies; only 32% of participants in a 2023 survey indicated that Mr. Biden had the "mental sharpness" for
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.046 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.016 | 0.027 |
| Insufficient payload (model declined to judge) | 0.007 | 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".