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Dynamic associations between daily acting with awareness and emotion regulation in individuals living with the effects of a stroke

2024· article· en· W4405386937 on OpenAlexaff
Nathaniel J. Johnson, Hali Kil, Theresa Pauly, Maureen C. Ashe, Kenneth Madden, Rachel A. Murphy, Wolfgang Linden, Denis Gerstorf, Christiane A. Hoppmann

Bibliographic record

VenueSocial Science & Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of British Columbia HospitalUniversity of British ColumbiaSimon Fraser University
Fundersnot available
KeywordsStroke (engine)Activities of daily livingGerontologyPsychologyMedicinePhysical medicine and rehabilitationPsychiatry

Abstract

fetched live from OpenAlex

= 68.70, SD = 10.56; range = 33-88; 26.7% female; 63.8% with less than college degree). Multilevel models examined the extent to which daily acting with awareness, previous-day negative affect, and previous-day positive affect were associated with daily negative and positive affect. Multilevel models operationally defined emotion regulation as affect carry-over, the extent to which affect lingered from one day to the next. Results revealed that on days when acting with awareness was elevated, negative affect did not carry over from the previous day, suggesting greater emotion regulation. Additionally, on days when acting with awareness was elevated, positive affect was maintained from day to day, indicating lingering positivity effects. Future research should expand upon our correlational findings, as the opposite causal direction might also hold-affect may increase the likelihood of acting with awareness. Overall, findings suggest that mindfulness-based interventions after stroke might benefit from a greater focus on daily acting with awareness.

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)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.310
Teacher spread0.299 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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