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Record W1953072024 · doi:10.1177/0733464815589987

Informing Understandings of Mild Cognitive Impairment for Older Adults: Implications From a Scoping Review

2015· review· en· W1953072024 on OpenAlexaff
Mei Lan Fang, Katherine Coatta, Melissa Badger, Sarah Wu, Margaret Easton, Louise Nygård, Arlene Astell, Andrew Sixsmith

Bibliographic record

VenueJournal of Applied Gerontology · 2015
Typereview
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of TorontoSimon Fraser University
FundersEconomic and Social Research Council
KeywordsConceptualizationPsychological interventionCognitive impairmentPsychologyGerontologyCognitionConceptual frameworkCognitive agingMedicineSociologyPsychiatrySocial science

Abstract

fetched live from OpenAlex

The development of effective interventions for mild cognitive impairment (MCI) in older adults has been limited by extensive variability in the conceptualization and definition of MCI, its subtypes, and relevant diagnostic criteria within the neurocultural, pharmaceutical, and gerontological communities. A scoping review was conducted to explore the conceptual development of MCI and identify the resulting ethical, political, and technological implications for the care of older adults with MCI. A comprehensive search was conducted between January and April 2013 to identify English-language peer-reviewed articles published between 1999 and 2013. Our analysis revealed that the MCI conceptual debate remains unresolved, the response to ethical issues is contentious, the policy response is limited, and one-dimensional and technological interventions are scarce. Reflections on the conceptual, ethical, and policy responses in conjunction with the identification of the needs of older adults diagnosed with MCI highlight significant opportunities for technological interventions to effectively reposition MCI in the aging care discourse.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.847
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
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 head0.125
GPT teacher head0.449
Teacher spread0.323 · 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 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

Citations25
Published2015
Admission routes1
Has abstractyes

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