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Record W4406799721 · doi:10.1080/24740527.2024.2440338

A regional program evaluation of the Stanford Chronic Pain Self-Management Program in Eastern Ontario, Canada

2025· article· en· W4406799721 on OpenAlexafffundabout
Emily Hum, Sathya Karunananthan, Abdullah Adil, Isabella Moroz, Rachel A. Davidson, Clare Liddy

Bibliographic record

VenueCanadian Journal of Pain · 2025
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsOttawa HospitalBruyèreUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsChronic painPain managementMedicinePhysical therapy

Abstract

fetched live from OpenAlex

Background: Health care providers often struggle to treat patients with chronic pain. One potential solution is to facilitate access to programs and tools that develop patients' skills and confidence in managing their own care. Aims: This study aimed to describe the uptake of the Chronic Pain Self-Management Program (CPSMP) in Eastern Ontario and evaluate the effectiveness of the program in the acquisition of knowledge, confidence, and skills required to manage chronic pain, as measured by the Patient Activation Measure (PAM). Methods: Using data routinely collected through the CPSMP between December 2017 and May 2023, we conducted a descriptive analysis of the number of participants each year, their gender, and their age distributions. We conducted a longitudinal analysis of the change in PAM score between participants' first (baseline) and last (follow-up) day in the program. Results: = 709) and 50- to 59-year-olds compared to other ages. Of the 1023 participants enrolled, 151 completed PAM surveys at baseline and follow-up (15%), of which 69% experienced an increase of at least 4 points on the PAM (104/151). Conclusion: Most participants were female and aged 50 to 59 years old. Among a sample of participants with available longitudinal data, the CPSMP demonstrated promising effectiveness at equipping participants with the knowledge, skills, and confidence to manage their pain. Replication in a larger representative sample is warranted.

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)0.006
metaresearch head score (Gemma)0.000
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 score0.690
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
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 head0.014
GPT teacher head0.281
Teacher spread0.266 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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