MétaCan
Menu
Back to cohort
Record W1193404116 · doi:10.1159/000381236

Frailty and Social Vulnerability

2015· review· en· W1193404116 on OpenAlexaff
Melissa K. Andrew

Bibliographic record

VenueInterdisciplinary topics in gerontology and geriatrics · 2015
Typereview
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGerontologyFraming (construction)Vulnerability (computing)Healthy agingComprehensionAffect (linguistics)PsychologyHealth careMedicineComputer sciencePolitical scienceEngineeringComputer security

Abstract

fetched live from OpenAlex

Both intrinsic and extrinsic factors contribute to health. Intrinsic factors are familiar topics in health research and include medical conditions, medications, genetics and frailty, while extrinsic factors stem from social and physical environments. This chapter builds on others in this volume, in which a deficit accumulation approach to frailty has been described. The concept of social vulnerability is presented. Social vulnerability stems from the accumulation of multiple and varied social problems and has bidirectional importance as a risk factor for poor health outcomes and as a pragmatic consideration for health care provision and planning. Importantly, the social factors that contribute to overall social vulnerability come into play at different levels of influence (individual, family and friends, peer groups, institutions and society at large). A social ecology perspective is discussed as a useful framework for considering social vulnerability, as it allows for attention to each of these levels of influence. Tying together what we currently understand about frailty (in medical and basic science models) and social vulnerability, the scaling potential of deficit accumulation is discussed, given that deficit accumulation can be understood to occur at many levels, from the (sub-)cellular level to tissues, organisms/complex systems and societies.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.124
GPT teacher head0.432
Teacher spread0.307 · 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 designNot applicable
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

Citations79
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueInterdisciplinary topics in gerontology and geriatricsSame topicFrailty in Older AdultsFrench-language works237,207