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Record W4393535837 · doi:10.1089/jicm.2023.0516

A Cross-Sectional Investigation of Trait Mindfulness, Concussion Symptom Severity, and Quality of Life in Adults with Persisting Symptoms Postconcussion

2024· article· en· W4393535837 on OpenAlexaff
Molly Cairncross, Andrée‐Anne Ledoux, Jonathan Greenberg, Noah D. Silverberg

Bibliographic record

VenueJournal of Integrative and Complementary Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of British ColumbiaCarleton UniversityUniversity of OttawaSimon Fraser University
FundersNational Center for Complementary and Integrative Health
KeywordsMindfulnessModerationQuality of life (healthcare)Association (psychology)Cross-sectional studyClinical psychologyConcussionPsychologyTraitMedicineInjury preventionPoison controlPsychotherapistMedical emergency

Abstract

fetched live from OpenAlex

Individual differences in mindfulness may impact quality of life after concussion. In a cross-sectional analysis, the moderating effect of mindfulness was tested on the association between symptom severity and quality of life in adults with persisting postconcussion symptoms ( N = 85). Mindfulness and symptom severity were independently associated with quality of life; however, mindfulness did not moderate this association. “Nonreactivity” was independently associated with quality of life; however, it was not a significant moderator. Taking a nonreactive stance, or allowing experiences to come and go without effort to change them, may be relevant to quality-of-life outcomes after concussion.

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)0.002
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.075
GPT teacher head0.380
Teacher spread0.305 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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