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Life Space in Older Adults

2018· reference-entry· en· W2887233134 on OpenAlexaff
Markus Wettstein, Hans‐Werner Wahl, Michael Schwenk

Bibliographic record

VenueOxford Research Encyclopedia of Psychology · 2018
Typereference-entry
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsSpace (punctuation)AutonomyQuality of life (healthcare)Everyday lifePerspective (graphical)PsychologyGerontologyGlobal Positioning SystemComputer scienceMedicineArtificial intelligencePolitical science

Abstract

fetched live from OpenAlex

Abstract When referring to life space, researchers usually mean the area in which individuals move in their everyday lives. Life space can be measured based on different approaches, by means of self-reports (i.e., questionnaires or diaries) or by more recent approaches of technology-based objective assessment (e.g., via Global Positioning System [GPS] devices or smartphones). Life space is an important indicator of older adults’ out-of-home mobility and is meaningfully associated with autonomy, well-being, and quality of life. Substantial relationships between life space and socio-demographic indicators, health, and cognitive abilities have been reported in previous research. Future research on life space in old age will benefit from a more comprehensive and stronger interdisciplinary perspective, from taking into account different time scales (i.e., short- and long-term variability), and from considering life space as a multidimensional measure that can be best assessed based on multi-method approaches with multiple indicators.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.078
GPT teacher head0.462
Teacher spread0.384 · 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 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

Citations8
Published2018
Admission routes1
Has abstractyes

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