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Record W2090631971 · doi:10.1097/ajp.0b013e31823853ac

Pain, Movement, and Mind

2012· article· en· W2090631971 on OpenAlexaff
Catherine M. Sabiston, Jennifer Brunet, Shaunna Burke

Bibliographic record

VenueClinical Journal of Pain · 2012
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineMovement (music)Physical medicine and rehabilitationAestheticsPhilosophy

Abstract

fetched live from OpenAlex

OBJECTIVES: This study examined the relationship between pain and mental health outcomes of depression and affect among survivors of breast cancer. The mediating role of physical activity was also tested. METHODS: Survivors of breast cancer (N=145) completed self-report measures of pain symptoms at baseline, wore an accelerometer for 7 days, and reported levels of depression symptoms and negative and positive affect 3 months later. Hierarchical linear regression analyses, controlling for personal and cancer-related demographics, were used to test the association between pain symptoms and each mental health outcome, as well as the mediation effect of physical activity. RESULTS: Pain positively predicted depression symptoms [F(6,139)=4.31, P<0.01, R=0.15] and negative affect [F(5,140)=4.17, P<0.01, R=0.13], and negatively predicted positive affect [F(6,139)=2.12, P=0.03, R=0.08]. Physical activity was a significant (P<0.01) partial mediator of the relationship between pain and depression and between pain and positive affect. DISCUSSION: Participation in physical activity is one pathway through which pain influences mental health. Efforts are needed to help survivors of breast cancer manage pain symptoms and increase their level of physical activity to help improve mental health.

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)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
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 head0.063
GPT teacher head0.387
Teacher spread0.324 · 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

Citations27
Published2012
Admission routes1
Has abstractyes

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