Bibliographic record
Abstract
This editorial highlights a modern paradox: insights from psychology and behavioral economics—originally developed to improve health—are now widely deployed in political campaigns, corporate advertising, and digital governance to steer choices, exploit cognitive shortcuts, and amplify compulsive engagement. It frames psychology as a double-edged sword in public health, noting strong evidence that psychological well-being protects physical health and that behavioral science can help populations make better choices. Yet, while “attention merchants” aggressively leverage these tools, public health systems underutilize proven approaches at scale. The result is an anxious, hyper-competitive environment shaped by status anxiety and hedonic adaptation, sustaining addictive consumption and emotional volatility. Ironically, the knowledge to counter these dynamics already exists—from “nudges” that structure healthier defaults to psychological levers that improve treatment adherence. Amid escalating threats—including climate instability, geopolitical risk, economic precarity, and the rapid advance of AI—the piece urges humanizing health and informatics systems and deploying transparent, population-level behavioral interventions that restore autonomy and well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.014 | 0.030 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".