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Record W4251810162 · doi:10.1093/geront/gnw162.2327

ESPO SOCIAL RESEARCH, POLICY, AND PRACTICE SECTION SYMPOSIUM: NEW DIMENSIONS IN IMPROVING CARE QUALITY IN LONG-TERM CARE

2016· article· en· W4251810162 on OpenAlexaff
Tomiko Yoneda, Jonathan Rush, Elizabeth Graham, Anne Ingeborg Berg, Nancy L. Pedersen, M Katz, Richard B. Lipton, Andrea M. Piccinin

Bibliographic record

VenueThe Gerontologist · 2016
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSection (typography)Term (time)Long-term careSocial careQuality (philosophy)PsychologyNursingSociologyMedicineComputer scienceEpistemology

Abstract

fetched live from OpenAlex

although its effectiveness is still very controversial.Some recent studies indicate that working memory capacity can indeed be enhanced through cognitive training interventions and that these gains transfer to other working-memoryrelated cognitive abilities such as fluid intelligence.However, others draw a more pessimistic picture concluding that working memory training does not generalize to other abilities.To assess training effectiveness we used a large study sample of 160 healthy older participants (aged 65 -80) who were randomly assigned to either a working memory training group or a visual search control group.Although we found significant improvements in working memory during and after training, no far transfer effects were found for reasoning and executive functions.Interestingly, previous cognitive training experience as well as personality traits such as neuroticism and grit predicted working memory improvements.

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)0.039
metaresearch head score (Gemma)0.046
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: Empirical · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.046
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.004
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0150.001

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 head0.198
GPT teacher head0.552
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 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
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

Citations0
Published2016
Admission routes1
Has abstractyes

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