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Record W2120951840 · doi:10.1017/s0144686x12001067

Physical capability and the advantages and disadvantages of ageing: perceptions of older age by men and women in two British cohorts

2012· article· en· W2120951840 on OpenAlexfundno aff
Samantha Parsons, Catharine R. Galé, Diana Kuh, Jane Elliott

Bibliographic record

VenueAgeing and Society · 2012
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
FundersEconomic and Social Research CouncilMedical Research CouncilAGE-WELL
KeywordsPerceptionOlder peopleAgeingGerontologyThird ageAgeing societyHealthy ageingPsychologyQualitative researchTheme (computing)Life course approachDevelopmental psychologySociologyMedicineSocial science

Abstract

fetched live from OpenAlex

ABSTRACT In an increasingly ageing society, its older members are receiving considerable political and policy attention. However, much remains to be learnt about public perceptions of older age, particularly the views and experiences of older individuals themselves. Drawing on qualitative interviews carried out with members of two British cohorts (N = 60) who have reached the ‘third age’, this paper discusses perceptions of age, focusing particularly on how perceived advantages and disadvantages differ by respondents' self-reported physical capability. The interviews were carried out in 2010 as part of the HALCyon (Healthy Ageing across the Life Course) collaborative research programme. Findings suggest there is some difference in the way older people view aspects of ageing by capability and that although advantages are widely perceived, physical decline and associated health concerns were the overwhelming theme across the conversations. The article concludes by making tentative suggestions to inform the positive ageing agenda and its related policies.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.344
Teacher spread0.334 · 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.

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

Citations15
Published2012
Admission routes1
Has abstractyes

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