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Record W4224012656 · doi:10.1016/j.arr.2022.101630

Life course socioeconomic conditions and multimorbidity in old age – A scoping review

2022· review· en· W4224012656 on OpenAlexaff
Cornelia Wagner, Cristian Carmeli, Arnaud Chioléro, Stéphane Cullati

Bibliographic record

VenueAgeing Research Reviews · 2022
Typereview
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsMcGill University
Fundersnot available
KeywordsLife course approachSocioeconomic statusDemographyYoung adultMultimorbidityPsychologyGerontologyEarly adulthoodDisadvantagePeriod (music)MedicineDevelopmental psychologyComorbidityPopulationPsychiatrySociology

Abstract

fetched live from OpenAlex

Multimorbidity disproportionally affects individuals exposed to socioeconomic disadvantage. It is, however, unclear how adverse socioeconomic conditions (SEC) at different periods of the life course predict the occurrence of multimorbidity in later life. In this scoping review, we investigate the association between life course SEC and later-life multimorbidity, and assess to which extent it supports different life course causal models (critical period, sensitive period, accumulation, pathway, or social mobility). We identified four studies (25,209 participants) with the first measure of SEC in childhood (before age 18). In these four studies, childhood SEC was associated with multimorbidity in old age, and the associations were partially or fully attenuated upon adjustment for later-life SEC. These results are consistent with the sensitive period and the pathway models. We identified five studies (91,236 participants) with the first measure of SEC in young adulthood (after age 18), and the associations with multimorbidity in old age as well as the effects of adjustment for later-life SEC differed from one study to the other. Among the nine included studies, none tested the social mobility or the accumulation models. In conclusion, SEC in early life could have an effect on multimorbidity, attenuated at least partly by SEC in adulthood.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.546
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.450
GPT teacher head0.563
Teacher spread0.113 · 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 teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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

Citations32
Published2022
Admission routes1
Has abstractyes

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