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Record W2163238516 · doi:10.1111/1468-0009.12105

Benefits Gained, Benefits Lost: Comparing Baby Boomers to Other Generations in a Longitudinal Cohort Study of Self‐Rated Health

2015· article· en· W2163238516 on OpenAlexafffundabout
Elizabeth M. Badley, Mayilée Cañizares, Anthony V. Perruccio, Sheilah Hogg‐Johnson, Monique A. M. Gignac

Bibliographic record

VenueMilbank Quarterly · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsInstitute for Work & HealthUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsBaby boomersDemographyCohortBody mass indexGerontologyMedicineContext (archaeology)Cohort effectBaby boomPopulationCohort studyEnvironmental healthDemographic economicsEconomicsGeography

Abstract

fetched live from OpenAlex

UNLABELLED: POLICY POINTS: Despite beliefs that baby boomers are healthier than previous generations, we found no evidence that the health of baby boomers is substantially different from that of the previous or succeeding cohorts. The effects of increased education, higher income, and lower smoking rates on improving self-rated health were nearly counterbalanced by the adverse effect of increasing body mass index (BMI). Assumptions that baby boomers will require less health care as they age because of better education, more prosperity, and less propensity to smoke may not be realized because of increases in obesity. CONTEXT: Baby boomers are commonly believed to be healthier than the previous generation. Using self-rated health (SRH) as an indicator of health status, this study examines the effects of age, period, and birth cohort on the trajectory of health across 4 generations: World War II (born between 1935 and 1944), older baby boomers (born between 1945 and 1954), younger baby boomers (born between 1955 and 1964), and Generation X (born between 1965 and 1974). METHODS: We analyzed Canada's longitudinal National Population Health Survey 1994-2010 (n = 8,570 at baseline), using multilevel growth models to estimate the age trajectory of SRH by cohort, accounting for period and incorporating the influence of changes in education, household income, smoking status, and body mass index (BMI) on SRH over time. FINDINGS: SRH worsened with increasing age in all cohorts. Cohort differences in SRH were modest (p = 0.034), but there was a significant period effect (p = 0.002). We found marked cohort effects for increasing education, income, and BMI, and decreasing smoking from the youngest to the oldest cohorts, which were much reduced (education and smoking) or removed (income and BMI) once period was taken into account. At the population level, multivariable analysis showed the benefits of increasing education and income and declines in smoking on the trajectory of improving SRH were almost counterbalanced by the effects of increasing BMI (obesity). CONCLUSIONS: We found no evidence to support the expectation that baby boomers will age more or less healthily than previous cohorts did. We also found that increasing BMI has likely undermined improvements in health that might have otherwise occurred, with possible implications for the need for health care. Period effects had a more profound effect than birth cohort effects. This suggests that interventions to improve health, such as reducing obesity, can be targeted to the entire, or a major portion of the, population and need not single out particular birth cohorts.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.195

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.084
GPT teacher head0.355
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations41
Published2015
Admission routes3
Has abstractyes

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