Reproducible materials for 'The Boomer Penalty: Excess Mortality among Baby Boomers in Canada and the United States'
Bibliographic record
Abstract
Studies suggest that baby boomers in Canada and the United States have experienced a slowdown, or even deterioration, in the all-cause mortality improvements relative to neighboring cohorts. These findings are counterintuitive and surprising. According to the technophysio evolution theory, the unprecedented improvements in early life conditions experienced by baby boomers should have led to declines in morbidity and mortality in later life, as was the case for generations born earlier. The present study explores mechanisms that could have produced the excess mortality for the baby boom cohorts in Canada and in three racial/ethnic groups in the United States. Using micro-level mortality data from vital statistics systems, we analyzed the contribution of causes of death that are likely driving this cohort’s excess mortality and their dynamic over time. The analyses are done using demographic decomposition, visual, and statistical methods. We found evidence of a higher susceptibility of the trailing edge boomers to behavioral causes of death, namely mortality from drugs, alcohol, HIV/AIDS, hepatitis C, COPD, and suicides. Most of these causes contributed to the all-cause mortality disadvantage of boomers by sustained cohort effects that escorted the cohorts over time. This invites a rethinking of the mechanisms driving current age-period-cohort mortality patterns. Mechanisms that can generate the observed cohort disadvantage, such as more prevalent levels of distress and frustration among boomers –the birth cohort effect proposed by Easterlin–, and the riskier attitudes toward drug use and sexual practices that are constituent of the boomer generation identity are addressed and discussed. https://www.demogr.mpg.de/en/publications_databases_6118/publications_1904/mpidr_working_papers/the_boomer_penalty_excess_mortality_among_baby_boomers_in_canada_and_the_united_states_6387/
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.067 | 0.010 |
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".