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Record W3089789894 · doi:10.46278/j.ncacn.20180505

Investigating a Mindfulness-Based Intervention as an Attentional Network Training to Improve Cognition in Older Adults with Amnestic Mild Cognitive Impairment: A Randomized-controlled Trial

2018· article· en· W3089789894 on OpenAlexaffvenue
Eddy Larouche, Carol Hudon, Sonia Goulet

Bibliographic record

VenueNeuropsychologie clinique et appliquée · 2018
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMindfulnessCognitive trainingPsychologyPsychoeducationCognitionCognitive remediation therapyPsychological interventionRandomized controlled trialCognitive InterventionIntervention (counseling)Attentional controlClinical psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Episodic memory deficits, often combined with impaired attention, are typical in older adults with amnestic mild cognitive impairment (aMCI). A Mindfulness-Based Intervention (MBI) could promote cognitive decline prevention or remediation through attentional network training. This randomized-controlled trial examined MBI’s effects on objective (tests) and subjective (self-reported) measures of memory and attention, compared to a Psychoeducation-Based Intervention (PBI), in 41 older adults with aMCI. No distinctive benefits of the MBI were observed on objective tests, with both interventions improving attentional control. Moreover, the appreciation of one’s cognitive functioning through questionnaires similarly improved for both interventions. Only in semi-structured interviews did a greater proportion of participants report benefits following the MBI compared to the PBI. This study does not provide sufficient support for the implementation of a MBI to enhance objective cognition by means of attentional network training in aMCI. However, it suggests a positive impact of non-pharmacological interventions on perceived cognition.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.035
GPT teacher head0.389
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.

Study designRandomized trial
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

Citations6
Published2018
Admission routes2
Has abstractyes

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