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Record W2548955063 · doi:10.1155/2016/4678083

Results from 10 Years of a CBT Pain Self-Management Outpatient Program for Complex Chronic Conditions

2016· article· en· W2548955063 on OpenAlexafffundabout
Kathryn Boschen, E. A. Robinson, Kent A. Campbell, Sarah M. Muir, Elvina Oey, Kristen Janes, Samantha Fashler, Joel Katz

Bibliographic record

VenuePain Research and Management · 2016
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsBridgepoint Active HealthcareYork UniversityUniversity of Toronto
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsChronic painPain managementMedicinePhysical therapyPsychologyPsychotherapistClinical psychology

Abstract

fetched live from OpenAlex

Background. Traditional unimodal interventions may be insufficient for treating complex pain, as they do not address cognitive and behavioural contributors to pain. Cognitive Behavioural Therapy (CBT) and physical exercise (PE) are empirically supported treatments that can reduce pain and improve quality of life. Objectives. To examine the outcomes of a pain self-management outpatient program based on CBT and PE at a rehabilitation hospital in Toronto, Ontario. Methods. The pain management group (PMG) consisted of 20 sessions over 10 weeks. The intervention consisted of four components: education, cognitive behavioural skills, exercise, and self-management strategies. Outcome measures included the sensory, affective, and intensity of pain experience, depression, anxiety, pain disability, active and passive coping style, and general health functioning. Results. From 2002 to 2011, 36 PMGs were run. In total, 311 patients entered the program and 214 completed it. Paired t -tests showed significant pre- to posttreatment improvements in all outcomes measured. Patient outcomes did not differ according to the number or type of diagnoses. Both before and after treatment, women reported more active coping than men. Discussion. The PMGs improved pain self-management for patients with complex pain. Future research should use a randomized controlled design to better understand the outcomes of PMGs.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.869
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
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 head0.043
GPT teacher head0.373
Teacher spread0.330 · 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 designNot applicable
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

Citations30
Published2016
Admission routes3
Has abstractyes

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