Virtual and in-person chronic pain management outcomes: A retrospective observational study
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic necessitated alternate modes of delivery of chronic pain management programs. The aim of this retrospective observational study was to examine the outcomes of virtual and in-person programs during the pandemic. Methods: Data were collected from the five-week intensive interdisciplinary pain management program (adapted for the pandemic) at the Michael G. DeGroote Pain Clinic, Hamilton, Ontario (N = 99; 66% virtual). Participants completed psychometric measures of pain intensity, pain disability, kinesiophobia, anxiety, depression, catastrophizing, sensitivity to pain traumatization, pain stages of change, pain acceptance, likelihood estimates of return to work and subjective happiness scales at admission and discharge, and self-evaluations of program benefit and satisfaction at discharge. A 2 × 2 mixed analysis of variance (ANOVA) on outcomes and between-groups ANOVAs on satisfaction measures were conducted. Results: > 0.05) either. Bonferroni correction was applied to the analysis. Discussion: Results show that virtual delivery of chronic pain management is just as effective as in-person delivery. Previous findings on the benefits of interdisciplinary chronic pain management are replicated. Results point to the need to have both programs available for patients to promote benefit for all patients.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".