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Record W1965213102 · doi:10.1016/j.pain.2009.08.030

Depressive symptoms and pain evaluations among persons with chronic pain: Catastrophizing, but not pain acceptance, shows significant effects

2009· article· en· W1965213102 on OpenAlexfundno aff
Elizabeth J. Richardson, Timothy J. Ness, Daniel M. Doleys, James H. Baños, Leanne R. Cianfrini, Scott J. Richards

Bibliographic record

VenuePain · 2009
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
FundersMcGill University
KeywordsPain catastrophizingChronic painAffect (linguistics)Depressive symptomsClinical psychologyCognitionPhysical therapyPsychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Cognitive factors such as catastrophic thoughts regarding pain, and conversely, one's acceptance of that pain, may affect emotional functioning among persons with chronic pain conditions. The aims of the present study were to examine the effects of both catastrophizing and acceptance on affective ratings of experimentally induced ischemic pain and also self-reports of depressive symptoms. Sixty-seven individuals with chronic back pain completed self-report measures of catastrophizing, acceptance, and depressive symptoms. In addition, participants underwent an ischemic pain induction procedure and were asked to rate the induced pain. Catastrophizing showed significant effects on sensory and intensity but not affective ratings of the induced pain. Acceptance did not show any significant associations, when catastrophizing was also in the model, with any form of ratings of the induced pain. Catastrophizing, but not acceptance, was also significantly associated with self-reported depressive symptoms when these two variables were both included in a regression model. Overall, results indicate negative thought patterns such as catastrophizing appear to be more closely related to outcomes of perceived pain severity and affect in persons with chronic pain exposed to an experimental laboratory pain stimulus than does more positive patterns as reflected in measures of acceptance.

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)0.014
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.006
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 head0.007
GPT teacher head0.261
Teacher spread0.253 · 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

Citations71
Published2009
Admission routes1
Has abstractyes

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