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Record W2155714366 · doi:10.1017/s1463423614000346

Mindfulness-Based Stress Reduction: pilot study of a treatment group for patients with chronic pain in a primary care setting

2014· article· en· W2155714366 on OpenAlexaffabout
Julie Beaulac, Matthew D. Bailly

Bibliographic record

VenuePrimary Health Care Research & Development · 2014
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMindfulness-based stress reductionMindfulnessMedicineChronic painPhysical therapyAttendanceDistressPain catastrophizingPrimary careStress reductionClinical psychologyFamily medicine

Abstract

fetched live from OpenAlex

AIM: The study objective was to evaluate an eight-week Mindfulness-Based Stress Reduction (MBSR) treatment group for chronic pain in terms of effects on pain disability, subjective ratings of pain and psychological distress related to pain, and activity level and willingness to experience pain. This pilot study evaluated the impact of two eight-week MBSR treatment groups that were delivered in a clinic in Winnipeg, Manitoba. BACKGROUND: Chronic pain is one of the most common presenting problems in primary care settings. METHODS: Adult patients with chronic pain were recruited from 20 clinics that are part of a collaborative care programme and outcome measures were administered at baseline and programme completion. FINDINGS: Despite a modest attendance rate and the short length of programme, a pre-post evaluation involving 17 patients revealed significant and/or clinically relevant improvements in level of pain disability, psychological distress, engagement in life activities, willingness to experience pain, and subjective rating of current pain.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.376
Teacher spread0.333 · 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 designNon-randomized trial
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

Citations11
Published2014
Admission routes2
Has abstractyes

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