THE EVOLUTION OF OUTPATIENT CHRONIC PAIN MANAGEMENT PROGRAMS: A MAPPING REVIEW
Bibliographic record
Abstract
Objective/Background: Multidisciplinary chronic pain management programs (PMPs) are regarded as the gold standard for people with chronic, noncancer pain. However, no consensus exists in the literature regarding the optimal content of PMPs. This study aims to amalgamate and describe outpatient PMPs between 2011-2020. Methods: Design: Systematic mapping review. Data Sources: CINAHL, OVIDMedline, PsychInfo, PEDro and Cochrane. Literature: Multidisciplinary PMPs for adults with chronic pain. Data extracted: Publication country, participants, providers, program content, approach, format, duration. Data synthesized: descriptively. Results: Publications: n=53; USA (21%), Australia (15%), and Canada (13%). Programs: n=51. Participants: n=10,182. Providers (% of publications): Physical therapists (94%), and Psychologists (83%), Medical practitioners (77%) were the most prevalent clinicians present. Program content (% of programs): Education (94%), Psychological therapy (90%), and Exercise/physical therapy (80%). Integrative therapies increased in prevalence from 2016-2020. Discussion/Conclusion: Content and format of PMPs evolved over time, specifically regarding medication education, general exercise, and integrative therapies demonstrated between 2016-2020. Implications: This investigation fills a void in the literature by establishing the standard components of PMP’s across the past decade and identifying changes in the care models in outpatient clinics. This mapping review provides an outline for future research.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.019 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".