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Record W2907642178 · doi:10.1080/13607863.2018.1544216

Mindful aging: The association between trait mindfulness and expectations regarding aging among middle-aged and older adults

2018· article· en· W2907642178 on OpenAlexaff
Alexandra Fiocco, Brad A. Meisner

Bibliographic record

VenueAging & Mental Health · 2018
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsYork UniversityToronto Metropolitan University
Fundersnot available
KeywordsPsychologyMindfulnessOpenness to experienceSuccessful agingFacet (psychology)TraitAssociation (psychology)Healthy agingClinical psychologyCuriosityBig Five personality traitsSocioeconomic statusGerontologyPersonalityDevelopmental psychologySocial psychologyMedicinePsychotherapist

Abstract

fetched live from OpenAlex

Objectives: Positive Expectations Regarding Aging serve as a protective factor of healthy aging; however, negative stereotypes regarding aging continue to dominate popular aging discourse. It is proposed that trait mindfulness (TM) is associated with aging expectations through the cultivation of openness, curiosity, and non-judgment to one’s thoughts, emotions, and sensations, whether they are positive or negative.Methods: Associations between the Five Facet Mindfulness Questionnaire and the Expectations Regarding Aging Survey (ERA-38) were examined among 201 participants aged 55+ years.Results: Analyses revealed that higher levels of TM was significantly associated with positive aging expectations, controlling for retirement and socioeconomic status (Rchange2= 14.0%, F(5,192) = 7.17, p < .001).Conclusion: The development of TM, notably the facet of non-judgment, may be used to promote positive aging expectations to help support healthy aging.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.328
Teacher spread0.300 · 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 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

Citations9
Published2018
Admission routes1
Has abstractyes

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