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Record W4200508041 · doi:10.1093/geroni/igab046.045

Effect of an eLearning Memory Program on Reducing Negative Impact of Age-Related Memory Changes

2021· article· en· W4200508041 on OpenAlexaff
Lynn Zhu, Danielle D’Amico, Iris Yusupov, Jordan Lass, Brian Levine, Susan Vandermorris, Angela K. Troyer

Bibliographic record

VenueInnovation in Aging · 2021
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsYork UniversityToronto Metropolitan UniversityBaycrest Hospital
Fundersnot available
KeywordsPsychoeducationPsychologyWorryRandomized controlled trialIntervention (counseling)GerontologyClinical psychologyMedicinePsychiatryAnxiety

Abstract

fetched live from OpenAlex

Abstract Age-related memory changes pose considerable concerns for aging adults, and can adversely affect their daily living and cause worry even when changes experienced are not clinically significant. The Memory and Aging Program® is a validated psychoeducation and memory strategy-training program that teaches the public about memory changes during aging and trains them to use evidence-based strategies to support brain health. The program has been offered in-person for over 20 years, and a self-guided eLearning version was recently developed to improve program accessibility. This study evaluated the self-reported impacts of memory changes in older adults who completed this eLearning against a control group. We randomized 202 older adults, without neurological or psychiatric diagnoses (71.6 years; 69 % female; 15.6 years of education), into the eLearning program or a control group that received no intervention. All participants reported their perceived impact of memory changes using the Memory Impact Questionnaire at pre-, post-, and 6-8 weeks follow-up. A significant reduction in negative impact of memory changes on daily living and a significant improvement in positive coping with memory changes relative to controls was observed at post-test (13.4 versus 2.5 points reduction and 7.4 versus 0.1 point improvement, respectively, both p < 0.05), but these did not persist at follow-up. The adoption of digital tools has hastened across all ages. Our study showed that self-guided digital tools, such as the eLearning Memory and Aging Program®, may be a promising avenue to help aging individuals reduce the impact of memory changes on daily living.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.021
GPT teacher head0.387
Teacher spread0.366 · 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 designBench or experimental
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

Citations2
Published2021
Admission routes1
Has abstractyes

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