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Record W2126979407 · doi:10.5539/jedp.v2n1p108

Principles of Longevity and Aging: Interventions to Enhance Older Adulthood

2012· article· en· W2126979407 on OpenAlexvenueno aff
Ryan Wessell, Carla Edwards

Bibliographic record

VenueJournal of Educational and Developmental Psychology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsnot available
Fundersnot available
KeywordsSuccessful agingPsychologyPsychological interventionGerontologyCoping (psychology)LongevityHealthy agingIntervention (counseling)Quality of life (healthcare)Affect (linguistics)MedicineClinical psychologyPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

Healthy aging and prevention efforts for the elderly warrant attention in a world where the average mortality rate continues to increase. The current literature review is an overview of current findings related to healthy aging and recommendations for older adults who are living longer and healthier; as well as facing the physical and psychological challenges that come with extended life. Staying active, eating right, utilizing social and environmental resources, employing coping skills developed across the lifespan, as well as developing new strategies can enhance the quality of life for older adults. Helping professionals from all disciplines who are able to recognize the needs of this growing group, and recognize the resiliency factors inherent in healthy aging, have the best chance of designing and implementing successful preventionand intervention efforts.The purpose of the present literature review is twofold: 1) To systematically review the important factors that affect an individual’s longevity and to raise awareness of the importance of those factors that are within one’s control; and 2) To inform health care providers of prevention efforts important to older adults;encouraging an integration of research and practice to preventative efforts.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.028
GPT teacher head0.354
Teacher spread0.327 · 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 designObservational
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

Citations3
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Educational and Developmental PsychologySame topicGenetics, Aging, and Longevity in Model OrganismsFrench-language works237,207