The utilization patterns and impact of the Stanford Chronic Pain Self-Management Program in Eastern Ontario, Canada
Bibliographic record
Abstract
Context Healthcare providers often struggle to treat patients with chronic pain. One potential solution is to facilitate patient access to programs that develop skills and confidence in managing their own care. Objectives: In this study, we aimed to describe patterns of utilization of a Chronic Pain Self-Management Program (CPSMP) in Eastern Ontario and evaluate the impact of the program on patient activation, a measure of participants’ involvement in their care, their health behaviors, and their knowledge of the condition. Study design and analysis: We conducted descriptive analysis of the number of participants registered each year, their gender and age distributions. We conducted longitudinal analyses of change in patient activation between participants’ enrollment in the program (baseline) and the end of the program (follow-up). Dataset: We used data routinely collected through the CPSMP between December 2017 and May 2023. Population studied: The CPSMP targeted individuals suffering from chronic pain in the Champlain region of Eastern Ontario. Anyone suffering from pain could register for the program without needing a referral or formal diagnosis from a health care practitioner. Intervention: The CPSMP is a six-week, peer-led program based on self-efficacy theory. The program explores themes such as sleep, managing difficult emotions, exercise, relaxation techniques, decision-making, problem-solving, and creating concrete action plans. Outcome Measures: Patient activation, measured via the validated Patient Activation Measure Results: 1023 individuals participated in the CPSMP during the study period. The number of participants peaked in 2018 and remained stable thereafter. There was a higher proportion of female (69%, n=709) compared to male participants and a higher proportion of 50-59-year-olds, compared to other age groups. Among the 151 participants (15% of the total sample) who completed a PAM survey at baseline and follow-up, 69% (104/151) experienced a clinically meaningful increase of four points on the PAM scale. Conclusions: Participation in the CPSMP resulted in a clinically meaningful increase in patient activation among patients with chronic pain. Since only 15% of CPSMP participants completed the PAM survey at baseline and follow-up, replication in a larger sample is warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".