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Record W2805931810 · doi:10.1177/1043659618776353

Uses and Perspectives of Aging Well Terminology in Taiwanese and International Literature: A Systematic Review

2018· review· en· W2805931810 on OpenAlexfundno aff
Shih-Ni Chen, Mary E. Riner, Joel Stocker, Min‐Tao Hsu

Bibliographic record

VenueJournal of Transcultural Nursing · 2018
Typereview
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
FundersAGE-WELL
KeywordsTerminologyDiversity (politics)Adaptation (eye)Interpretation (philosophy)Scientific literaturePsychologySystematic reviewQualitative researchSociologyLinguisticsMEDLINESocial sciencePolitical scienceAnthropology

Abstract

fetched live from OpenAlex

The aim of this study is to examine aging well (AW) terminology in Taiwan in its local and global contexts, and to suggest ways of communication by Taiwanese professionals that is sensitive to the lay public's preferences. Researchers conducted a systematic review using Khan et al.'s strategy, and Harden and Thomas' method, to sift through seven databases and synthesize diverse studies on AW. Primary aging well terms used in English and Chinese, their usage frequency in Taiwanese academia, and one term uniquely used by lay people in Taiwan were identified. The synthesized literature illustrated commonality as well as diversity in use and interpretation of aging well terms within Taiwanese society and compared with the Western-based research. More qualitative research is needed to explore how AW is experienced, interpreted, and expected from lay perspectives in Taiwan and other countries have primarily relied on translation and adaptation of Western terms in their scientific research.

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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.166
Threshold uncertainty score0.625

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 science0.0000.000
Research integrity0.0000.001
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 head0.095
GPT teacher head0.448
Teacher spread0.354 · 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 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

Citations2
Published2018
Admission routes1
Has abstractyes

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