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Record W2108524054 · doi:10.3384/ijal.1652-8670.094123

Successful aging as an oxymoron

2009· article· en· W2108524054 on OpenAlexfundno aff
Sandra Torres, Gunhild Hammarström

Bibliographic record

VenueInternational Journal of Ageing and Later Life · 2009
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
FundersUppsala UniversitetVetenskapsrådetAGE-WELL
KeywordsConstruct (python library)OxymoronOlder peoplePsychologySocial psychologyGerontologyMedicinePublic relationsPolitical science

Abstract

fetched live from OpenAlex

Notions of what it means to age well or successfully are central to social gerontological research and practice. As such, one would expect that there would be consensus as to what the construct of successful aging means and/or how aging well is achieved. This is not, however, the case which is why this study explores the meanings that a group of older people (i.e. some with home-help care and some without) attach to this construct. The empirical material is constituted of 16 semi-structured interviews. The findings bring to fore the different resources (such as physical, mental, psycho-social, spiritual, and financial ones) that are associated with successful aging and the kind of outlook on life that is regarded as useful if one wants to age well. Differences between home-help care recipients and those that do not receive this type of care were found. Those that are managing without the help offered by home-help care services listed more resources and offered more nuanced descriptions of what successful aging means than those that receive home- help care. This suggests that receiving home-help care and/or not being able to manage primarily on one’s own might shape the manner in which older people think about what constitutes a good old age. The in-depth analysis of the notions of successful aging that were brought to the fore suggests also the paradoxical fact that the title of this article attests to; namely that some associate aging well with not aging at all and deem, in fact, the term successful aging to be an oxymoron.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.027
GPT teacher head0.388
Teacher spread0.361 · 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

Citations32
Published2009
Admission routes1
Has abstractyes

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