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Record W3149080796 · doi:10.1177/13591053211002326

Self-compassion as predictor of daily physical symptoms and chronic illness across older adulthood

2021· article· en· W3149080796 on OpenAlexafffund
Heather Herriot, Carsten Wrosch

Bibliographic record

VenueJournal of Health Psychology · 2021
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsConcordia University
FundersCanadian Institutes of Health Research
KeywordsSelf-compassionPhysical illnessPsychologyMedicineClinical psychologyGerontologyPhysical activityIllness behaviorPsychiatryCognitionMindfulnessPhysical therapy

Abstract

fetched live from OpenAlex

This study examined whether self-compassion could benefit daily physical symptoms and chronic illness in early and advanced old age. The hypotheses were evaluated in a 4-year longitudinal study of 264 older adults. Results showed that self-compassion predicted lower levels of daily physical symptoms across the study period in advanced, but not early, old age ( T-ratio = −1.93, p = 0.05). In addition, self-compassion was associated with fewer increases in chronic illness in advanced, but not early, old age ( T-ratio = − 2.45, p < 0.02). The results of this study suggest that self-compassion may be particularly adaptive towards the end of life.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.021
GPT teacher head0.409
Teacher spread0.388 · 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 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

Citations13
Published2021
Admission routes2
Has abstractyes

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