Why Deaths of Despair Are Increasing in the US and Not Other Industrial Nations—Insights From Neuroscience and Anthropology
Bibliographic record
Abstract
The US National Academy of Sciences reports rising mortality for US adults, most steeply for White adults with a secondary education or less. The rise is largely attributable to deaths of despair (suicide and poisoning by alcohol and drugs) with strong contributions from the cardiovascular effects of rising obesity. Although the report does acknowledge a crisis, it proposes mild measures to manage it, such as strengthening programs to support recovery, prevent relapse, increase resilience, and perform more research toward clinically useful definitions of despair. The US National Academy of Sciences report notes that mortality is decreasing in a control group of 16 wealthy nations (including countries in Western Europe, Canada, Australia, and Japan), but it does not ask what protects those nations from despair. It has been observed that human beings are constrained by evolutionary strategy (ie, huge brain, prolonged physical and emotional dependence, education beyond adolescence for professional skills, and extended adult learning) to require communal support at all stages of the life cycle. Without support, difficulties accumulate until there seems to be no way forward. The 16 wealthy nations provide communal assistance at every stage, thus facilitating diverse paths forward and protecting individuals and families from despair. The US could solve its health crisis by adopting the best practices of the 16-nation control group.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".